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Working on machine learning, feature selection, causal discovery, bioinformatics, artificial intelligence.<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="24434052" href="https://www.academia.edu/Documents/in/Generalized_Linear_models"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://crete.academia.edu/IoannisTsamardinos","location":"/IoannisTsamardinos","scheme":"https","host":"crete.academia.edu","port":null,"pathname":"/IoannisTsamardinos","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" 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class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="R-packages" data-toggle="tab" href="#rpackages" role="tab" title="R packages"><span>2</span> <span class="ds2-5-body-sm-bold">R packages</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Ioannis Tsamardinos</h3></div><div class="js-work-strip profile--work_container" data-work-id="37454058"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER"><img alt="Research paper thumbnail of Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER" class="work-thumbnail" src="https://attachments.academia-assets.com/57421925/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER">Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div class="wp-workCard_item"><span>F1000Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Feature (or variable) selection is the process of identifying the minimal set of features with th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eef61111acd5f13151f6338fa65fa368" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":57421925,"asset_id":37454058,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="37454058"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="37454058"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37454058; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37454058]").text(description); $(".js-view-count[data-work-id=37454058]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37454058; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='37454058']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 37454058, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eef61111acd5f13151f6338fa65fa368" } } $('.js-work-strip[data-work-id=37454058]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":37454058,"title":"Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER","translated_title":"","metadata":{"abstract":"Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.","publication_name":"F1000Research"},"translated_abstract":"Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.","internal_url":"https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER","translated_internal_url":"","created_at":"2018-09-21T04:17:04.995-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":31894239,"work_id":37454058,"tagging_user_id":71523,"tagged_user_id":24434052,"co_author_invite_id":null,"email":"t***t@gmail.com","affiliation":"University of Crete","display_order":1,"name":"Ioannis Tsamardinos","title":"Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER"}],"downloadable_attachments":[{"id":57421925,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/57421925/thumbnails/1.jpg","file_name":"Feature_selection_with_the_R_package_MXM.pdf","download_url":"https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Open_Peer_Review_Feature_selection_with.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/57421925/Feature_selection_with_the_R_package_MXM-libre.pdf?1537528985=\u0026response-content-disposition=attachment%3B+filename%3DOpen_Peer_Review_Feature_selection_with.pdf\u0026Expires=1732121159\u0026Signature=ZQsYfOUwtTqoAMoh2weHwh4YqQp~tWcMVm39rCmT~IueVTVJnk12KksyZ5qyW7A7QbOxIeNIxBM29DTwdvDwUwlgI9kwph-~3zvIB1IloJTxI7XxH0DWciSqTJvOCejgRRheYTGihr6mTMriJTdVNp8tajESJLuKXC9B2pK0O8-835on8h42HdVkAK8zvCnmtwwWq~~ftFaaPXLHP~NYyIMYMzx4~M5ccl1slAIpSymQAVG3nCss72ziHSfcZq6l4E6gQgvcqktx4CuSwpSKX9rUJQ--KH9o3WklXnWSmRsfS8tSFrvokpR0R3AbkLZoWbTCS6J9gjP27sS2lOl9pA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":71523,"first_name":"螠喂蠂伪萎位","middle_initials":null,"last_name":"韦蟽伪纬蟻萎蟼","page_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","domain_name":"crete","created_at":"2009-10-14T01:59:37.339-07:00","display_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","url":"https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82"},"attachments":[{"id":57421925,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/57421925/thumbnails/1.jpg","file_name":"Feature_selection_with_the_R_package_MXM.pdf","download_url":"https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Open_Peer_Review_Feature_selection_with.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/57421925/Feature_selection_with_the_R_package_MXM-libre.pdf?1537528985=\u0026response-content-disposition=attachment%3B+filename%3DOpen_Peer_Review_Feature_selection_with.pdf\u0026Expires=1732121159\u0026Signature=ZQsYfOUwtTqoAMoh2weHwh4YqQp~tWcMVm39rCmT~IueVTVJnk12KksyZ5qyW7A7QbOxIeNIxBM29DTwdvDwUwlgI9kwph-~3zvIB1IloJTxI7XxH0DWciSqTJvOCejgRRheYTGihr6mTMriJTdVNp8tajESJLuKXC9B2pK0O8-835on8h42HdVkAK8zvCnmtwwWq~~ftFaaPXLHP~NYyIMYMzx4~M5ccl1slAIpSymQAVG3nCss72ziHSfcZq6l4E6gQgvcqktx4CuSwpSKX9rUJQ--KH9o3WklXnWSmRsfS8tSFrvokpR0R3AbkLZoWbTCS6J9gjP27sS2lOl9pA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":85879,"name":"Variable Selection","url":"https://www.academia.edu/Documents/in/Variable_Selection"},{"id":1012702,"name":"R Packages","url":"https://www.academia.edu/Documents/in/R_Packages"}],"urls":[{"id":8590734,"url":"https://f1000researchdata.s3.amazonaws.com/manuscripts/17707/23556c28-b06b-4e5c-bbbc-d213c74c0880_16216_-_michail_tsagris.pdf?doi=10.12688/f1000research.16216.1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="35742078"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data"><img alt="Research paper thumbnail of Feature selection for high-dimensional temporal data" class="work-thumbnail" src="https://attachments.academia-assets.com/55616743/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data">Feature selection for high-dimensional temporal data</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/VLagani">Vincenzo Lagani</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div class="wp-workCard_item"><span>BMC Bioinformatics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Feature selection is commonly employed for identifying collectively-predictive biomar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional " omics " temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65ca8cba623a6284e3a2b57a97cd7259" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55616743,"asset_id":35742078,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="35742078"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="35742078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35742078; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35742078]").text(description); $(".js-view-count[data-work-id=35742078]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35742078; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='35742078']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 35742078, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "65ca8cba623a6284e3a2b57a97cd7259" } } $('.js-work-strip[data-work-id=35742078]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":35742078,"title":"Feature selection for high-dimensional temporal data","translated_title":"","metadata":{"doi":"10.1186/s12859-018-2023-7","abstract":"Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional \" omics \" temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"BMC Bioinformatics"},"translated_abstract":"Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional \" omics \" temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.","internal_url":"https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data","translated_internal_url":"","created_at":"2018-01-23T12:10:51.614-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":30964159,"work_id":35742078,"tagging_user_id":71523,"tagged_user_id":33460367,"co_author_invite_id":null,"email":"v***i@yahoo.it","display_order":1,"name":"Vincenzo Lagani","title":"Feature selection for high-dimensional temporal data"},{"id":30964160,"work_id":35742078,"tagging_user_id":71523,"tagged_user_id":24434052,"co_author_invite_id":null,"email":"t***t@gmail.com","affiliation":"University of Crete","display_order":2,"name":"Ioannis Tsamardinos","title":"Feature selection for high-dimensional temporal data"}],"downloadable_attachments":[{"id":55616743,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/55616743/thumbnails/1.jpg","file_name":"Feature_selection_for_high_dimensional_temporal_data_-_2018.pdf","download_url":"https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Feature_selection_for_high_dimensional_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/55616743/Feature_selection_for_high_dimensional_temporal_data_-_2018-libre.pdf?1516738774=\u0026response-content-disposition=attachment%3B+filename%3DFeature_selection_for_high_dimensional_t.pdf\u0026Expires=1732266103\u0026Signature=B6wC8cbiVfjbLekYNSVBfjJPV1XDziepN3O0Wr0cjVD7saK67UPI2bP8fuld699WjsYMn4yENQU87kz5JMPkSULCDf2eg7Z8vj0tXRUF-agWyJs~~oCBRwuk3rjF1QEHce27pPnTl8uOim03XAyPLLrpFOWZX4pbZT6k0uI2DlnwuObY5iWtJdY4G2aK~BB9rPo~6WEZnlm-wmacJo1~tEDeeD~epj4b2D-uYqOhjppIcO15CxOy~jsZA4Ht3nMal7Wx6hy6E5BCsJfhVR4oki9kM8s3wXd22GxNeyj1HDxk99ijOf7NsqGT13ZTNzTO9QdC7SA~2zITcCQX9dpk4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Feature_selection_for_high_dimensional_temporal_data","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":71523,"first_name":"螠喂蠂伪萎位","middle_initials":null,"last_name":"韦蟽伪纬蟻萎蟼","page_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","domain_name":"crete","created_at":"2009-10-14T01:59:37.339-07:00","display_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","url":"https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82"},"attachments":[{"id":55616743,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/55616743/thumbnails/1.jpg","file_name":"Feature_selection_for_high_dimensional_temporal_data_-_2018.pdf","download_url":"https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Feature_selection_for_high_dimensional_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/55616743/Feature_selection_for_high_dimensional_temporal_data_-_2018-libre.pdf?1516738774=\u0026response-content-disposition=attachment%3B+filename%3DFeature_selection_for_high_dimensional_t.pdf\u0026Expires=1732266103\u0026Signature=B6wC8cbiVfjbLekYNSVBfjJPV1XDziepN3O0Wr0cjVD7saK67UPI2bP8fuld699WjsYMn4yENQU87kz5JMPkSULCDf2eg7Z8vj0tXRUF-agWyJs~~oCBRwuk3rjF1QEHce27pPnTl8uOim03XAyPLLrpFOWZX4pbZT6k0uI2DlnwuObY5iWtJdY4G2aK~BB9rPo~6WEZnlm-wmacJo1~tEDeeD~epj4b2D-uYqOhjppIcO15CxOy~jsZA4Ht3nMal7Wx6hy6E5BCsJfhVR4oki9kM8s3wXd22GxNeyj1HDxk99ijOf7NsqGT13ZTNzTO9QdC7SA~2zITcCQX9dpk4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="26594306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/26594306/Towards_Robust_and_Versatile_Causal_Discovery_for_Business_Applications"><img alt="Research paper thumbnail of Towards Robust and Versatile Causal Discovery for Business Applications" class="work-thumbnail" src="https://attachments.academia-assets.com/46882579/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/26594306/Towards_Robust_and_Versatile_Causal_Discovery_for_Business_Applications">Towards Robust and Versatile Causal Discovery for Business Applications</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/GBorboudakis">Giorgos Borboudakis</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Causal discovery algorithms can induce some of the causal relations from the data, commonly in th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however , all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above.The code is available on the mensxmachina.org website.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6bc65eeaf9f359f3a1e566aa4108ebae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46882579,"asset_id":26594306,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46882579/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="26594306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="26594306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26594306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26594306]").text(description); $(".js-view-count[data-work-id=26594306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26594306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26594306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26594306, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6bc65eeaf9f359f3a1e566aa4108ebae" } } $('.js-work-strip[data-work-id=26594306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26594306,"title":"Towards Robust and Versatile Causal Discovery for Business Applications","translated_title":"","metadata":{"abstract":"Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however , all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above.The code is available on the mensxmachina.org website."},"translated_abstract":"Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however , all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25733523"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets"><img alt="Research paper thumbnail of Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets" class="work-thumbnail" src="https://attachments.academia-assets.com/54339180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets">Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/VLagani">Vincenzo Lagani</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The statistically equivalent signature (SES) algorithm is a method for feature selection inspired...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c88c2d7ea7ece2f8e2c02d2daa72325" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":54339180,"asset_id":25733523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/54339180/download_file?st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25733523"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25733523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25733523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25733523]").text(description); $(".js-view-count[data-work-id=25733523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25733523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='25733523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 25733523, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8c88c2d7ea7ece2f8e2c02d2daa72325" } } $('.js-work-strip[data-work-id=25733523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":25733523,"title":"Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets","translated_title":"","metadata":{"abstract":"The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.","journal_name":"Journal of Statistical software"},"translated_abstract":"The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.","internal_url":"https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets","translated_internal_url":"","created_at":"2016-05-30T22:02:46.521-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":20786147,"work_id":25733523,"tagging_user_id":71523,"tagged_user_id":6338583,"co_author_invite_id":null,"email":"g***s@hotmail.com","affiliation":"Foundation for Research and Technology - Hellas","display_order":0,"name":"Giorgos Athineou","title":"Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature 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similarity features and a two-phase decision tree" class="work-thumbnail" src="https://attachments.academia-assets.com/39207490/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16833515/Morphological_classification_of_heartbeats_using_similarity_features_and_a_two_phase_decision_tree">Morphological classification of heartbeats using similarity features and a two-phase decision tree</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitraEmmanouilidou">Dimitra Emmanouilidou</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div 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Plan execution can make good use of that flexibility. However, the advantage of execution flexibility is counterbalanced by the cost during execution of propagating the time of occurrence of events throughout the flexible plan. To minimize execution latency, this propagation needs to be very efficient. Previous work showed that every temporal plan can be reformulated as a dispatchable plan, i.e., one for which propagation to immediate neighbors is sufficient. A simple algorithm was given that finds a dispatchable plan with a minimum number of edges in cubic time and quadratic space. In this paper, we focus on the efficiency of the reformulation process, and improve on that result. A new algorithm is presented that uses linear space and has time complexity equivalent to Johnson's algorithm for all-pairs shortest-path problems. Experimental evidence confirms the practical effectiveness of the new algorithm. For example, on a large commercial application, the performance is improved by at least two orders of magnitude. We further show that the dispatchable plan, already minimal in the total number of edges, can also be made minimal in the maximum number of edges incoming or outgoing at any node. *","grobid_abstract_attachment_id":39170674},"translated_abstract":null,"internal_url":"https://www.academia.edu/16764537/Fast_Transformation_of_Temporal_Plans_for_Efficient_Execution","translated_internal_url":"","created_at":"2015-10-13T23:42:10.965-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170674,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170674/thumbnails/1.jpg","file_name":"AAAI98-035.pdf","download_url":"https://www.academia.edu/attachments/39170674/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fast_Transformation_of_Temporal_Plans_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170674/AAAI98-035-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DFast_Transformation_of_Temporal_Plans_fo.pdf\u0026Expires=1732388432\u0026Signature=FTCODeZnKw6VA6lwJXzVhVSAhaQld2QkEnV6fQfMsBoYgaP7eQfRnaSSlhuts3QIXoZU0-j6zht6rHrnya8bplyFFEISmjdQ22MO1usTgSAlrvDNXoaxRA9UVU-etZ9wfUA5hfmCNgVKaHzWN-6sZ2Dq8kb42CP7lJS3acAp7uymCfijCmcc47h4~bCxTrRIVk0pK4CeV4p5TSzvyDpPT25oGTrNbmfuWSIoJEJL06Trz4jZPxsrVh66GPvvWDv9UU1aWHObmYWeLPKnYJeYIfDutc~fh0bvZYNYz9rYgYoNdE6fl1UuHENsgbXn6Q8qOeK1k94qSeXJfa7V1ysIYw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Fast_Transformation_of_Temporal_Plans_for_Efficient_Execution","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170674,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170674/thumbnails/1.jpg","file_name":"AAAI98-035.pdf","download_url":"https://www.academia.edu/attachments/39170674/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Fast_Transformation_of_Temporal_Plans_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170674/AAAI98-035-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DFast_Transformation_of_Temporal_Plans_fo.pdf\u0026Expires=1732388432\u0026Signature=FTCODeZnKw6VA6lwJXzVhVSAhaQld2QkEnV6fQfMsBoYgaP7eQfRnaSSlhuts3QIXoZU0-j6zht6rHrnya8bplyFFEISmjdQ22MO1usTgSAlrvDNXoaxRA9UVU-etZ9wfUA5hfmCNgVKaHzWN-6sZ2Dq8kb42CP7lJS3acAp7uymCfijCmcc47h4~bCxTrRIVk0pK4CeV4p5TSzvyDpPT25oGTrNbmfuWSIoJEJL06Trz4jZPxsrVh66GPvvWDv9UU1aWHObmYWeLPKnYJeYIfDutc~fh0bvZYNYz9rYgYoNdE6fl1UuHENsgbXn6Q8qOeK1k94qSeXJfa7V1ysIYw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":209855,"name":"Time Complexity","url":"https://www.academia.edu/Documents/in/Time_Complexity"},{"id":1906509,"name":"All Pairs Shortest Path","url":"https://www.academia.edu/Documents/in/All_Pairs_Shortest_Path"}],"urls":[]}, dispatcherData: dispatcherData }); 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However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle &quot;orphan&quot; features (not belonging to a cluster). T-ReCS can be used wi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99e1066b697860a967e72d1c6c88e87a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170666,"asset_id":16764534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170666/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764534; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764534]").text(description); $(".js-view-count[data-work-id=16764534]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764534; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764534']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764534, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "99e1066b697860a967e72d1c6c88e87a" } } $('.js-work-strip[data-work-id=16764534]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764534,"title":"T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES","translated_title":"","metadata":{"abstract":"Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle \u0026quot;orphan\u0026quot; features (not belonging to a cluster). T-ReCS can be used wi..."},"translated_abstract":"Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle \u0026quot;orphan\u0026quot; features (not belonging to a cluster). T-ReCS can be used wi...","internal_url":"https://www.academia.edu/16764534/T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_FORMED_GROUPS_OF_FEATURES_WITH_APPLICATION_TO_PREDICTION_OF_CLINICAL_OUTCOMES","translated_internal_url":"","created_at":"2015-10-13T23:42:10.648-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170666,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170666/thumbnails/1.jpg","file_name":"542e56ff0cf277d58e8ea220.pdf","download_url":"https://www.academia.edu/attachments/39170666/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_F.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170666/542e56ff0cf277d58e8ea220-libre.pdf?1444807037=\u0026response-content-disposition=attachment%3B+filename%3DT_RECS_STABLE_SELECTION_OF_DYNAMICALLY_F.pdf\u0026Expires=1732388432\u0026Signature=JqCQGv194T8DO1llmNYeYzN8btXj3TXleQUPRVi95nEJJg1Ppk4S-AaI7yC8Lb8O6KUnSFnoJf2jygXgnVPGETwpUNIZqe34HDc3xG4P4xMb4qX6YIWv6NvKC65fLa9apJ8jN2gJl8ENFLMeHtjVAnHvYEhgmtb5z2FFyfvIntKsjVbvMonGPKx-cpmd5i~kPB0yGbUQ3QaoSuomWk~HCjK9rJJM01yQkR3fqyoINHHbBQM2DgPWq9iWItm1vDvy0o9mcydQIv-ebvF8fKfZlTgRg4zA7tzjPZZetTeIfxkIAvbEV5qEXO4NBxeoCrQ~z5FASXlyArWjAhQ6atB6ew__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_FORMED_GROUPS_OF_FEATURES_WITH_APPLICATION_TO_PREDICTION_OF_CLINICAL_OUTCOMES","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170666,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170666/thumbnails/1.jpg","file_name":"542e56ff0cf277d58e8ea220.pdf","download_url":"https://www.academia.edu/attachments/39170666/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_F.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170666/542e56ff0cf277d58e8ea220-libre.pdf?1444807037=\u0026response-content-disposition=attachment%3B+filename%3DT_RECS_STABLE_SELECTION_OF_DYNAMICALLY_F.pdf\u0026Expires=1732388432\u0026Signature=JqCQGv194T8DO1llmNYeYzN8btXj3TXleQUPRVi95nEJJg1Ppk4S-AaI7yC8Lb8O6KUnSFnoJf2jygXgnVPGETwpUNIZqe34HDc3xG4P4xMb4qX6YIWv6NvKC65fLa9apJ8jN2gJl8ENFLMeHtjVAnHvYEhgmtb5z2FFyfvIntKsjVbvMonGPKx-cpmd5i~kPB0yGbUQ3QaoSuomWk~HCjK9rJJM01yQkR3fqyoINHHbBQM2DgPWq9iWItm1vDvy0o9mcydQIv-ebvF8fKfZlTgRg4zA7tzjPZZetTeIfxkIAvbEV5qEXO4NBxeoCrQ~z5FASXlyArWjAhQ6atB6ew__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":25780,"name":"Biocomputing","url":"https://www.academia.edu/Documents/in/Biocomputing"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764533"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764533/Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine"><img alt="Research paper thumbnail of Text categorization models for high-quality article retrieval in internal medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/39170665/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764533/Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine">Text categorization models for high-quality article retrieval in internal medicine</a></div><div class="wp-workCard_item"><span>Journal of the American Medical Informatics Association</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exce...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e98599796f973f0c67ac4ed3ed1d3f4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170665,"asset_id":16764533,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170665/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764533"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764533"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764533; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764533]").text(description); $(".js-view-count[data-work-id=16764533]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764533; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764533']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764533, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e98599796f973f0c67ac4ed3ed1d3f4f" } } $('.js-work-strip[data-work-id=16764533]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764533,"title":"Text categorization models for high-quality article retrieval in internal medicine","translated_title":"","metadata":{"abstract":"OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average ...","publication_name":"Journal of the American Medical Informatics Association"},"translated_abstract":"OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764532"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty"><img alt="Research paper thumbnail of Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty" class="work-thumbnail" src="https://attachments.academia-assets.com/39170680/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty">Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Temporal uncertainty is a feature of many real-world planning problems. One of the most successfu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u&#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec1a7bd6861084421b9369811c211397" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170680,"asset_id":16764532,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764532"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764532"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764532; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764532]").text(description); $(".js-view-count[data-work-id=16764532]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764532; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764532']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764532, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec1a7bd6861084421b9369811c211397" } } $('.js-work-strip[data-work-id=16764532]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764532,"title":"Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty","translated_title":"","metadata":{"abstract":"Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u\u0026#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter..."},"translated_abstract":"Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u\u0026#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter...","internal_url":"https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty","translated_internal_url":"","created_at":"2015-10-13T23:42:10.431-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170680,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170680/thumbnails/1.jpg","file_name":"00b49520e1396c30c0000000.pdf","download_url":"https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_Probability_of_Legal_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170680/00b49520e1396c30c0000000-libre.pdf?1444807033=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Probability_of_Legal_Execu.pdf\u0026Expires=1732388432\u0026Signature=XQ41Ne8c95cXwK70sRhIAP~cRc2MMbqQ7CAj-YkqShiBny7cesPwhgzg6RgQQ39j0YClQF2AIpNeE8Faat9vOYPF56oq8wCYvJd~ynZE8aFI3sfTZhNi7qMRMRigt7wn-pWl1jNF8xD6XKiqP3exrPbgOjy4kpiti6e2PqNaw1kq7DsC91P4y7MV64Jg9Rh6dLK15yKvgctMeoC43bQE3xl0xVeK3PrPIEibkAqYz7MFbz0AfoTvUAtmDA7noYa7~n9n00hP0HmGMZ7pUqlP2Tv2xksbChidTYrwbSCspdGAXT-gep0ssQTwkQd1U0dUlqtANled-RM-FzufqJeMyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170680,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170680/thumbnails/1.jpg","file_name":"00b49520e1396c30c0000000.pdf","download_url":"https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_Probability_of_Legal_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170680/00b49520e1396c30c0000000-libre.pdf?1444807033=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Probability_of_Legal_Execu.pdf\u0026Expires=1732388432\u0026Signature=XQ41Ne8c95cXwK70sRhIAP~cRc2MMbqQ7CAj-YkqShiBny7cesPwhgzg6RgQQ39j0YClQF2AIpNeE8Faat9vOYPF56oq8wCYvJd~ynZE8aFI3sfTZhNi7qMRMRigt7wn-pWl1jNF8xD6XKiqP3exrPbgOjy4kpiti6e2PqNaw1kq7DsC91P4y7MV64Jg9Rh6dLK15yKvgctMeoC43bQE3xl0xVeK3PrPIEibkAqYz7MFbz0AfoTvUAtmDA7noYa7~n9n00hP0HmGMZ7pUqlP2Tv2xksbChidTYrwbSCspdGAXT-gep0ssQTwkQd1U0dUlqtANled-RM-FzufqJeMyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764531"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764531/Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning"><img alt="Research paper thumbnail of Discovering and Exploiting Entailement Relationships in Multi-Label Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/39170670/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764531/Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning">Discovering and Exploiting Entailement Relationships in Multi-Label Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work presents a sound probabilistic method for enforcing adherence of the marginal probabili...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailement: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented with a Bayesian network. Marginal probabilities are entered as soft evidence in the network and adjusted through probabilistic inference. Our approach offers robust improvements in mean average precision compared to the standard binary relavance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="33c86efcf8b5002b7dd562514171953c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170670,"asset_id":16764531,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170670/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764531"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764531"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764531; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764531]").text(description); $(".js-view-count[data-work-id=16764531]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764531; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764531']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764531, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "33c86efcf8b5002b7dd562514171953c" } } $('.js-work-strip[data-work-id=16764531]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764531,"title":"Discovering and Exploiting Entailement Relationships in Multi-Label Learning","translated_title":"","metadata":{"abstract":"This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. 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The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself."},"translated_abstract":"This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailement: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented with a Bayesian network. Marginal probabilities are entered as soft evidence in the network and adjusted through probabilistic inference. Our approach offers robust improvements in mean average precision compared to the standard binary relavance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.","internal_url":"https://www.academia.edu/16764531/Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning","translated_internal_url":"","created_at":"2015-10-13T23:42:10.305-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170670,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170670/thumbnails/1.jpg","file_name":"55130a1c0cf23203199ac14d.pdf","download_url":"https://www.academia.edu/attachments/39170670/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Discovering_and_Exploiting_Entailement_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170670/55130a1c0cf23203199ac14d-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DDiscovering_and_Exploiting_Entailement_R.pdf\u0026Expires=1732388432\u0026Signature=ZJsgbD6u1v-6wqr78XYW5xBNZ2NqgumPaw8zvckwS4kDVUUy42Ll1HKYLlDiJm-jy0bYJOPqKEImx7ztBPPOjTbZ7yr1OvTxDsitwrKHWuWTPwUN8wY3YhxcTXgrjLeE2W4LjpRfv0IplwUz2BWGqXRVgeoc-4Y-v8-sf1ViLt4sJ-G9h0e1xcs~RjIIzzVMGIn85E16V5zQNTelPVkpYk44unI6hV7Bg6S4NJHOIXGQ9XpnwoEAMDDqdX5eb8hjNB7bE9JUM1ArLo37Y3KL1lofx5Fy2ikKK-OYJg38BcUx~XmbB3q20ddn2NznWtj2Z2qTp1zzhYlZjUcFiyQWhA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning","translated_slug":"","page_count":16,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170670,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170670/thumbnails/1.jpg","file_name":"55130a1c0cf23203199ac14d.pdf","download_url":"https://www.academia.edu/attachments/39170670/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Discovering_and_Exploiting_Entailement_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170670/55130a1c0cf23203199ac14d-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DDiscovering_and_Exploiting_Entailement_R.pdf\u0026Expires=1732388432\u0026Signature=ZJsgbD6u1v-6wqr78XYW5xBNZ2NqgumPaw8zvckwS4kDVUUy42Ll1HKYLlDiJm-jy0bYJOPqKEImx7ztBPPOjTbZ7yr1OvTxDsitwrKHWuWTPwUN8wY3YhxcTXgrjLeE2W4LjpRfv0IplwUz2BWGqXRVgeoc-4Y-v8-sf1ViLt4sJ-G9h0e1xcs~RjIIzzVMGIn85E16V5zQNTelPVkpYk44unI6hV7Bg6S4NJHOIXGQ9XpnwoEAMDDqdX5eb8hjNB7bE9JUM1ArLo37Y3KL1lofx5Fy2ikKK-OYJg38BcUx~XmbB3q20ddn2NznWtj2Z2qTp1zzhYlZjUcFiyQWhA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764530"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764530/Identifying_Markov_Blankets_with_Decision_Tree_Induction"><img alt="Research paper thumbnail of Identifying Markov Blankets with Decision Tree Induction" class="work-thumbnail" src="https://attachments.academia-assets.com/39170669/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764530/Identifying_Markov_Blankets_with_Decision_Tree_Induction">Identifying Markov Blankets with Decision Tree Induction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Markov blanket of a target variable is the minimum conditioning set of variables that makes t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Markov blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 &#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bfba3a84b436ecdcb0ef9952c4776d1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170669,"asset_id":16764530,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764530"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764530"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764530; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764530]").text(description); $(".js-view-count[data-work-id=16764530]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764530; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764530']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764530, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bfba3a84b436ecdcb0ef9952c4776d1f" } } $('.js-work-strip[data-work-id=16764530]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764530,"title":"Identifying Markov Blankets with Decision Tree Induction","translated_title":"","metadata":{"abstract":"The Markov blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 \u0026#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared."},"translated_abstract":"The Markov blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 \u0026#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.","internal_url":"https://www.academia.edu/16764530/Identifying_Markov_Blankets_with_Decision_Tree_Induction","translated_internal_url":"","created_at":"2015-10-13T23:42:10.194-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170669,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170669/thumbnails/1.jpg","file_name":"00b7d514b3a2d3247f000000.pdf","download_url":"https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Identifying_Markov_Blankets_with_Decisio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170669/00b7d514b3a2d3247f000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DIdentifying_Markov_Blankets_with_Decisio.pdf\u0026Expires=1732388432\u0026Signature=aXs8sfKw2t4RAnyOjWoAYDAqLJ6qV23J07TyGa9NBHUTJ5VLPX~X68oeSeTohs-hVGw-FlLfXWy7AEmR8p7qOpfsBwI6r~67D~sFlVWuKOkjBaP8Aa4rfnUCxqVWFeUedwq8IjHy9elAgLwq0xJo7C6MLPNek9s2usth2xoEhEhpft2ADj8tlslujpH9WhtGh2XHBKU29qoZQhgvpZ1KNtIvXUq4z0Gi7JBN-vqtpmMz~T9mQeIWd1ndnVefcxlQftRsWQSl~7wNqquOCXRPYJavpayTvynGcIouPirTkQqpDTJCPE76XxzIX69zDEC~PmfBhinSk6n97QKLCyI5HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Identifying_Markov_Blankets_with_Decision_Tree_Induction","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170669,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170669/thumbnails/1.jpg","file_name":"00b7d514b3a2d3247f000000.pdf","download_url":"https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Identifying_Markov_Blankets_with_Decisio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170669/00b7d514b3a2d3247f000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DIdentifying_Markov_Blankets_with_Decisio.pdf\u0026Expires=1732388432\u0026Signature=aXs8sfKw2t4RAnyOjWoAYDAqLJ6qV23J07TyGa9NBHUTJ5VLPX~X68oeSeTohs-hVGw-FlLfXWy7AEmR8p7qOpfsBwI6r~67D~sFlVWuKOkjBaP8Aa4rfnUCxqVWFeUedwq8IjHy9elAgLwq0xJo7C6MLPNek9s2usth2xoEhEhpft2ADj8tlslujpH9WhtGh2XHBKU29qoZQhgvpZ1KNtIvXUq4z0Gi7JBN-vqtpmMz~T9mQeIWd1ndnVefcxlQftRsWQSl~7wNqquOCXRPYJavpayTvynGcIouPirTkQqpDTJCPE76XxzIX69zDEC~PmfBhinSk6n97QKLCyI5HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":101465,"name":"Causal Discovery","url":"https://www.academia.edu/Documents/in/Causal_Discovery"},{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764529"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16764529/A_Methodological_Framework_for_Statistical_Analysis_of_Social_Text_Streams"><img alt="Research paper thumbnail of A Methodological Framework for Statistical Analysis of Social Text Streams" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/16764529/A_Methodological_Framework_for_Statistical_Analysis_of_Social_Text_Streams">A Methodological Framework for Statistical Analysis of Social Text Streams</a></div><div class="wp-workCard_item"><span>Communications in Computer and Information Science</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764529"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764529"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764529; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764529]").text(description); $(".js-view-count[data-work-id=16764529]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764529; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764529']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764529, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16764529]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764529,"title":"A Methodological Framework for Statistical Analysis of Social Text Streams","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Communications in Computer and Information Science"},"translated_abstract":null,"internal_url":"https://www.academia.edu/16764529/A_Methodological_Framework_for_Statistical_Analysis_of_Social_Text_Streams","translated_internal_url":"","created_at":"2015-10-13T23:42:10.092-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Methodological_Framework_for_Statistical_Analysis_of_Social_Text_Streams","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764528"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764528/A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty"><img alt="Research paper thumbnail of A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty" class="work-thumbnail" src="https://attachments.academia-assets.com/42402146/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764528/A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty">A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. In real domains how- ever, this assumption is commonly violated and certain events are beyond the di- rect control of the plan&amp;#x27;s executive. Previous work on reasoning with uncontrol- lable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) fol- lowing a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dy- namic schedule where scheduling decisions can be made on-line.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="782f7c4458556e3290a672e1217cfa4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42402146,"asset_id":16764528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42402146/download_file?st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764528]").text(description); $(".js-view-count[data-work-id=16764528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "782f7c4458556e3290a672e1217cfa4f" } } $('.js-work-strip[data-work-id=16764528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764528,"title":"A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty","translated_title":"","metadata":{"abstract":"In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764526"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764526/Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System"><img alt="Research paper thumbnail of Execution-Time Plan Management for a Cognitive Orthotic System" class="work-thumbnail" src="https://attachments.academia-assets.com/39170671/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764526/Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System">Execution-Time Plan Management for a Cognitive Orthotic System</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2002</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3bd7da3ce672529a1dfb3caa01b6589" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170671,"asset_id":16764526,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170671/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764526"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764526"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764526; 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Autominder is being designed as part of an initiative on the development of robotic assistants for the elderly. Autominder stores and updates user plans, tracks their execution via input from robot sensors, and provides carefully chosen and timed reminders of the activities to be performed. It will eventually also learn the typical behavior of the user with regard to the execution of these plans. A central component of Autominder is its Plan Manager (PM), which is responsible for the temporal reasoning involved in updating plans and tracking their execution. The PM models plan update problems as disjunctive temporal problems (DTPs) and uses the Epilitis DTPsolving system to handle them. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764525"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764525/MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology"><img alt="Research paper thumbnail of MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764525/MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology">MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology</a></div><div class="wp-workCard_item"><span>PLOS ONE</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764525"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764525"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764525; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764525]").text(description); $(".js-view-count[data-work-id=16764525]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764525; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764525']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764525, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16764525]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764525,"title":"MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology","translated_title":"","metadata":{"abstract":"We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"PLOS ONE"},"translated_abstract":"We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.","internal_url":"https://www.academia.edu/16764525/MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology","translated_internal_url":"","created_at":"2015-10-13T23:42:09.674-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[],"research_interests":[{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":220780,"name":"PLoS one","url":"https://www.academia.edu/Documents/in/PLoS_one"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2374593" id="papers"><div class="js-work-strip profile--work_container" data-work-id="37454058"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER"><img alt="Research paper thumbnail of Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER" class="work-thumbnail" src="https://attachments.academia-assets.com/57421925/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER">Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div class="wp-workCard_item"><span>F1000Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Feature (or variable) selection is the process of identifying the minimal set of features with th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eef61111acd5f13151f6338fa65fa368" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":57421925,"asset_id":37454058,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="37454058"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="37454058"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37454058; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37454058]").text(description); $(".js-view-count[data-work-id=37454058]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37454058; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='37454058']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 37454058, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eef61111acd5f13151f6338fa65fa368" } } $('.js-work-strip[data-work-id=37454058]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":37454058,"title":"Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER","translated_title":"","metadata":{"abstract":"Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.","publication_name":"F1000Research"},"translated_abstract":"Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.","internal_url":"https://www.academia.edu/37454058/Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER","translated_internal_url":"","created_at":"2018-09-21T04:17:04.995-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":31894239,"work_id":37454058,"tagging_user_id":71523,"tagged_user_id":24434052,"co_author_invite_id":null,"email":"t***t@gmail.com","affiliation":"University of Crete","display_order":1,"name":"Ioannis Tsamardinos","title":"Open Peer Review Feature selection with the R package MXM Referee Status: AWAITING PEER"}],"downloadable_attachments":[{"id":57421925,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/57421925/thumbnails/1.jpg","file_name":"Feature_selection_with_the_R_package_MXM.pdf","download_url":"https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Open_Peer_Review_Feature_selection_with.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/57421925/Feature_selection_with_the_R_package_MXM-libre.pdf?1537528985=\u0026response-content-disposition=attachment%3B+filename%3DOpen_Peer_Review_Feature_selection_with.pdf\u0026Expires=1732121159\u0026Signature=ZQsYfOUwtTqoAMoh2weHwh4YqQp~tWcMVm39rCmT~IueVTVJnk12KksyZ5qyW7A7QbOxIeNIxBM29DTwdvDwUwlgI9kwph-~3zvIB1IloJTxI7XxH0DWciSqTJvOCejgRRheYTGihr6mTMriJTdVNp8tajESJLuKXC9B2pK0O8-835on8h42HdVkAK8zvCnmtwwWq~~ftFaaPXLHP~NYyIMYMzx4~M5ccl1slAIpSymQAVG3nCss72ziHSfcZq6l4E6gQgvcqktx4CuSwpSKX9rUJQ--KH9o3WklXnWSmRsfS8tSFrvokpR0R3AbkLZoWbTCS6J9gjP27sS2lOl9pA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Open_Peer_Review_Feature_selection_with_the_R_package_MXM_Referee_Status_AWAITING_PEER","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":71523,"first_name":"螠喂蠂伪萎位","middle_initials":null,"last_name":"韦蟽伪纬蟻萎蟼","page_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","domain_name":"crete","created_at":"2009-10-14T01:59:37.339-07:00","display_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","url":"https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82"},"attachments":[{"id":57421925,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/57421925/thumbnails/1.jpg","file_name":"Feature_selection_with_the_R_package_MXM.pdf","download_url":"https://www.academia.edu/attachments/57421925/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Open_Peer_Review_Feature_selection_with.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/57421925/Feature_selection_with_the_R_package_MXM-libre.pdf?1537528985=\u0026response-content-disposition=attachment%3B+filename%3DOpen_Peer_Review_Feature_selection_with.pdf\u0026Expires=1732121159\u0026Signature=ZQsYfOUwtTqoAMoh2weHwh4YqQp~tWcMVm39rCmT~IueVTVJnk12KksyZ5qyW7A7QbOxIeNIxBM29DTwdvDwUwlgI9kwph-~3zvIB1IloJTxI7XxH0DWciSqTJvOCejgRRheYTGihr6mTMriJTdVNp8tajESJLuKXC9B2pK0O8-835on8h42HdVkAK8zvCnmtwwWq~~ftFaaPXLHP~NYyIMYMzx4~M5ccl1slAIpSymQAVG3nCss72ziHSfcZq6l4E6gQgvcqktx4CuSwpSKX9rUJQ--KH9o3WklXnWSmRsfS8tSFrvokpR0R3AbkLZoWbTCS6J9gjP27sS2lOl9pA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":85879,"name":"Variable Selection","url":"https://www.academia.edu/Documents/in/Variable_Selection"},{"id":1012702,"name":"R Packages","url":"https://www.academia.edu/Documents/in/R_Packages"}],"urls":[{"id":8590734,"url":"https://f1000researchdata.s3.amazonaws.com/manuscripts/17707/23556c28-b06b-4e5c-bbbc-d213c74c0880_16216_-_michail_tsagris.pdf?doi=10.12688/f1000research.16216.1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="35742078"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data"><img alt="Research paper thumbnail of Feature selection for high-dimensional temporal data" class="work-thumbnail" src="https://attachments.academia-assets.com/55616743/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data">Feature selection for high-dimensional temporal data</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/VLagani">Vincenzo Lagani</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div class="wp-workCard_item"><span>BMC Bioinformatics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Feature selection is commonly employed for identifying collectively-predictive biomar...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional " omics " temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65ca8cba623a6284e3a2b57a97cd7259" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55616743,"asset_id":35742078,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="35742078"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="35742078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35742078; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35742078]").text(description); $(".js-view-count[data-work-id=35742078]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35742078; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='35742078']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 35742078, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "65ca8cba623a6284e3a2b57a97cd7259" } } $('.js-work-strip[data-work-id=35742078]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":35742078,"title":"Feature selection for high-dimensional temporal data","translated_title":"","metadata":{"doi":"10.1186/s12859-018-2023-7","abstract":"Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional \" omics \" temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"BMC Bioinformatics"},"translated_abstract":"Background: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional \" omics \" temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. Results: The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data.","internal_url":"https://www.academia.edu/35742078/Feature_selection_for_high_dimensional_temporal_data","translated_internal_url":"","created_at":"2018-01-23T12:10:51.614-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":30964159,"work_id":35742078,"tagging_user_id":71523,"tagged_user_id":33460367,"co_author_invite_id":null,"email":"v***i@yahoo.it","display_order":1,"name":"Vincenzo Lagani","title":"Feature selection for high-dimensional temporal data"},{"id":30964160,"work_id":35742078,"tagging_user_id":71523,"tagged_user_id":24434052,"co_author_invite_id":null,"email":"t***t@gmail.com","affiliation":"University of Crete","display_order":2,"name":"Ioannis Tsamardinos","title":"Feature selection for high-dimensional temporal data"}],"downloadable_attachments":[{"id":55616743,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/55616743/thumbnails/1.jpg","file_name":"Feature_selection_for_high_dimensional_temporal_data_-_2018.pdf","download_url":"https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Feature_selection_for_high_dimensional_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/55616743/Feature_selection_for_high_dimensional_temporal_data_-_2018-libre.pdf?1516738774=\u0026response-content-disposition=attachment%3B+filename%3DFeature_selection_for_high_dimensional_t.pdf\u0026Expires=1732266103\u0026Signature=B6wC8cbiVfjbLekYNSVBfjJPV1XDziepN3O0Wr0cjVD7saK67UPI2bP8fuld699WjsYMn4yENQU87kz5JMPkSULCDf2eg7Z8vj0tXRUF-agWyJs~~oCBRwuk3rjF1QEHce27pPnTl8uOim03XAyPLLrpFOWZX4pbZT6k0uI2DlnwuObY5iWtJdY4G2aK~BB9rPo~6WEZnlm-wmacJo1~tEDeeD~epj4b2D-uYqOhjppIcO15CxOy~jsZA4Ht3nMal7Wx6hy6E5BCsJfhVR4oki9kM8s3wXd22GxNeyj1HDxk99ijOf7NsqGT13ZTNzTO9QdC7SA~2zITcCQX9dpk4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Feature_selection_for_high_dimensional_temporal_data","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":71523,"first_name":"螠喂蠂伪萎位","middle_initials":null,"last_name":"韦蟽伪纬蟻萎蟼","page_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","domain_name":"crete","created_at":"2009-10-14T01:59:37.339-07:00","display_name":"螠喂蠂伪萎位韦蟽伪纬蟻萎蟼","url":"https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82"},"attachments":[{"id":55616743,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/55616743/thumbnails/1.jpg","file_name":"Feature_selection_for_high_dimensional_temporal_data_-_2018.pdf","download_url":"https://www.academia.edu/attachments/55616743/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Feature_selection_for_high_dimensional_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/55616743/Feature_selection_for_high_dimensional_temporal_data_-_2018-libre.pdf?1516738774=\u0026response-content-disposition=attachment%3B+filename%3DFeature_selection_for_high_dimensional_t.pdf\u0026Expires=1732266103\u0026Signature=B6wC8cbiVfjbLekYNSVBfjJPV1XDziepN3O0Wr0cjVD7saK67UPI2bP8fuld699WjsYMn4yENQU87kz5JMPkSULCDf2eg7Z8vj0tXRUF-agWyJs~~oCBRwuk3rjF1QEHce27pPnTl8uOim03XAyPLLrpFOWZX4pbZT6k0uI2DlnwuObY5iWtJdY4G2aK~BB9rPo~6WEZnlm-wmacJo1~tEDeeD~epj4b2D-uYqOhjppIcO15CxOy~jsZA4Ht3nMal7Wx6hy6E5BCsJfhVR4oki9kM8s3wXd22GxNeyj1HDxk99ijOf7NsqGT13ZTNzTO9QdC7SA~2zITcCQX9dpk4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="26594306"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/26594306/Towards_Robust_and_Versatile_Causal_Discovery_for_Business_Applications"><img alt="Research paper thumbnail of Towards Robust and Versatile Causal Discovery for Business Applications" class="work-thumbnail" src="https://attachments.academia-assets.com/46882579/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/26594306/Towards_Robust_and_Versatile_Causal_Discovery_for_Business_Applications">Towards Robust and Versatile Causal Discovery for Business Applications</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/GBorboudakis">Giorgos Borboudakis</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Causal discovery algorithms can induce some of the causal relations from the data, commonly in th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however , all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above.The code is available on the mensxmachina.org website.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6bc65eeaf9f359f3a1e566aa4108ebae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46882579,"asset_id":26594306,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46882579/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="26594306"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="26594306"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26594306; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26594306]").text(description); $(".js-view-count[data-work-id=26594306]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26594306; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26594306']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26594306, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6bc65eeaf9f359f3a1e566aa4108ebae" } } $('.js-work-strip[data-work-id=26594306]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26594306,"title":"Towards Robust and Versatile Causal Discovery for Business Applications","translated_title":"","metadata":{"abstract":"Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. 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ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above.The code is available on the mensxmachina.org website."},"translated_abstract":"Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however , all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25733523"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets"><img alt="Research paper thumbnail of Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets" class="work-thumbnail" src="https://attachments.academia-assets.com/54339180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets">Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/VLagani">Vincenzo Lagani</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The statistically equivalent signature (SES) algorithm is a method for feature selection inspired...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c88c2d7ea7ece2f8e2c02d2daa72325" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":54339180,"asset_id":25733523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/54339180/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25733523"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25733523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25733523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25733523]").text(description); $(".js-view-count[data-work-id=25733523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25733523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='25733523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 25733523, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8c88c2d7ea7ece2f8e2c02d2daa72325" } } $('.js-work-strip[data-work-id=25733523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":25733523,"title":"Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets","translated_title":"","metadata":{"abstract":"The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.","journal_name":"Journal of Statistical software"},"translated_abstract":"The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of pre-dictive accuracy and that multiple, equally predictive signatures are actually present in real world data.","internal_url":"https://www.academia.edu/25733523/Feature_Selection_with_the_R_Package_MXM_Discovering_Statistically_Equivalent_Feature_Subsets","translated_internal_url":"","created_at":"2016-05-30T22:02:46.521-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":71523,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":20786147,"work_id":25733523,"tagging_user_id":71523,"tagged_user_id":6338583,"co_author_invite_id":null,"email":"g***s@hotmail.com","affiliation":"Foundation for Research and Technology - Hellas","display_order":0,"name":"Giorgos Athineou","title":"Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature 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similarity features and a two-phase decision tree" class="work-thumbnail" src="https://attachments.academia-assets.com/39207490/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16833515/Morphological_classification_of_heartbeats_using_similarity_features_and_a_two_phase_decision_tree">Morphological classification of heartbeats using similarity features and a two-phase decision tree</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DimitraEmmanouilidou">Dimitra Emmanouilidou</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div 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Plan execution can make good use of that flexibility. However, the advantage of execution flexibility is counterbalanced by the cost during execution of propagating the time of occurrence of events throughout the flexible plan. To minimize execution latency, this propagation needs to be very efficient. Previous work showed that every temporal plan can be reformulated as a dispatchable plan, i.e., one for which propagation to immediate neighbors is sufficient. A simple algorithm was given that finds a dispatchable plan with a minimum number of edges in cubic time and quadratic space. In this paper, we focus on the efficiency of the reformulation process, and improve on that result. A new algorithm is presented that uses linear space and has time complexity equivalent to Johnson's algorithm for all-pairs shortest-path problems. Experimental evidence confirms the practical effectiveness of the new algorithm. For example, on a large commercial application, the performance is improved by at least two orders of magnitude. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764534"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764534/T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_FORMED_GROUPS_OF_FEATURES_WITH_APPLICATION_TO_PREDICTION_OF_CLINICAL_OUTCOMES"><img alt="Research paper thumbnail of T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES" class="work-thumbnail" src="https://attachments.academia-assets.com/39170666/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764534/T_RECS_STABLE_SELECTION_OF_DYNAMICALLY_FORMED_GROUPS_OF_FEATURES_WITH_APPLICATION_TO_PREDICTION_OF_CLINICAL_OUTCOMES">T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Feature selection is used extensively in biomedical research for biomarker identification and pat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle &quot;orphan&quot; features (not belonging to a cluster). T-ReCS can be used wi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99e1066b697860a967e72d1c6c88e87a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170666,"asset_id":16764534,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170666/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764534"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764534"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764534; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764534]").text(description); $(".js-view-count[data-work-id=16764534]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764534; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764534']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764534, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "99e1066b697860a967e72d1c6c88e87a" } } $('.js-work-strip[data-work-id=16764534]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764534,"title":"T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES","translated_title":"","metadata":{"abstract":"Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764533"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764533/Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine"><img alt="Research paper thumbnail of Text categorization models for high-quality article retrieval in internal medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/39170665/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764533/Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine">Text categorization models for high-quality article retrieval in internal medicine</a></div><div class="wp-workCard_item"><span>Journal of the American Medical Informatics Association</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exce...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e98599796f973f0c67ac4ed3ed1d3f4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170665,"asset_id":16764533,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170665/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764533"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764533"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764533; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764533]").text(description); $(".js-view-count[data-work-id=16764533]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764533; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764533']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764533, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e98599796f973f0c67ac4ed3ed1d3f4f" } } $('.js-work-strip[data-work-id=16764533]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764533,"title":"Text categorization models for high-quality article retrieval in internal medicine","translated_title":"","metadata":{"abstract":"OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. 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MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average ...","publication_name":"Journal of the American Medical Informatics Association"},"translated_abstract":"OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average ...","internal_url":"https://www.academia.edu/16764533/Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine","translated_internal_url":"","created_at":"2015-10-13T23:42:10.538-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170665,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170665/thumbnails/1.jpg","file_name":"02e7e51e7c593b3fc3000000.pdf","download_url":"https://www.academia.edu/attachments/39170665/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Text_categorization_models_for_high_qual.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170665/02e7e51e7c593b3fc3000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DText_categorization_models_for_high_qual.pdf\u0026Expires=1732388432\u0026Signature=BtFl5H8dIo2Pr54bQgogjFca91NeEirotZER9F-pIB-jO2uEN3zrTHmhGJlTiCyq5iSgkYkf~TPLC1Z6jp0GTrrYggWYeIRaknvCbYgjzrSmkpF2JehcjJvGvatv-GeicdLZDnkUD9c0MKghr-zLaOGTlwtweCEz6GjGFyrzR1AKLYSwfyqW9toSh-jtmIMkaroFX-Swg3dQXw-CEjAdrJH4pRb3a9vPP5W6gveifUh2TRcYEDHzb~xzlpipf7zD~xRwOMr1vECByJ8XDpxpTHvN9QYaUtzxMxsqG4m-TZxQgGrSp1uQji2K~PaY5vOp3MId3xpler5nyn0tx98Uxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Text_categorization_models_for_high_quality_article_retrieval_in_internal_medicine","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170665,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170665/thumbnails/1.jpg","file_name":"02e7e51e7c593b3fc3000000.pdf","download_url":"https://www.academia.edu/attachments/39170665/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Text_categorization_models_for_high_qual.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170665/02e7e51e7c593b3fc3000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DText_categorization_models_for_high_qual.pdf\u0026Expires=1732388432\u0026Signature=BtFl5H8dIo2Pr54bQgogjFca91NeEirotZER9F-pIB-jO2uEN3zrTHmhGJlTiCyq5iSgkYkf~TPLC1Z6jp0GTrrYggWYeIRaknvCbYgjzrSmkpF2JehcjJvGvatv-GeicdLZDnkUD9c0MKghr-zLaOGTlwtweCEz6GjGFyrzR1AKLYSwfyqW9toSh-jtmIMkaroFX-Swg3dQXw-CEjAdrJH4pRb3a9vPP5W6gveifUh2TRcYEDHzb~xzlpipf7zD~xRwOMr1vECByJ8XDpxpTHvN9QYaUtzxMxsqG4m-TZxQgGrSp1uQji2K~PaY5vOp3MId3xpler5nyn0tx98Uxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":65390,"name":"Internal Medicine","url":"https://www.academia.edu/Documents/in/Internal_Medicine"},{"id":194916,"name":"ROC Curve","url":"https://www.academia.edu/Documents/in/ROC_Curve"},{"id":203527,"name":"Medline","url":"https://www.academia.edu/Documents/in/Medline"},{"id":255453,"name":"Information Storage and Retrieval","url":"https://www.academia.edu/Documents/in/Information_Storage_and_Retrieval"},{"id":900542,"name":"Selection Bias","url":"https://www.academia.edu/Documents/in/Selection_Bias"},{"id":1309706,"name":"Area Under Curve","url":"https://www.academia.edu/Documents/in/Area_Under_Curve"},{"id":1863718,"name":"The American","url":"https://www.academia.edu/Documents/in/The_American"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764532"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty"><img alt="Research paper thumbnail of Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty" class="work-thumbnail" src="https://attachments.academia-assets.com/39170680/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty">Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Temporal uncertainty is a feature of many real-world planning problems. One of the most successfu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u&#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec1a7bd6861084421b9369811c211397" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170680,"asset_id":16764532,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764532"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764532"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764532; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764532]").text(description); $(".js-view-count[data-work-id=16764532]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764532; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764532']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764532, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ec1a7bd6861084421b9369811c211397" } } $('.js-work-strip[data-work-id=16764532]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764532,"title":"Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty","translated_title":"","metadata":{"abstract":"Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u\u0026#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter..."},"translated_abstract":"Temporal uncertainty is a feature of many real-world planning problems. One of the most successful formalisms for dealing with temporal uncertainty is the Simple Temporal Problem with uncertainty (STP-u). A very attractive feature of STP-u\u0026#39;s is that one can determine in polynomial time whether a given STP-u is dynamically controllable, i.e., whether there is a guaranteed means of execution such that all the constraints are respected, regardless of the exact timing of the uncertain events. Unfortunately, if the STP-u is not dynamically controllable, limitations of the formalism prevent further reasoning about the probability of legal execution. In this paper, we present an alternative formalism, called Probabilistic Simple Temporal Problems (PSTPs), which generalizes STP-u to allow for such reasoning. We show that while it is difficult to compute the exact probability of legal execution, there are methods for bounding the probability both from above and below, and we sketch alter...","internal_url":"https://www.academia.edu/16764532/Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty","translated_internal_url":"","created_at":"2015-10-13T23:42:10.431-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170680,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170680/thumbnails/1.jpg","file_name":"00b49520e1396c30c0000000.pdf","download_url":"https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_Probability_of_Legal_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170680/00b49520e1396c30c0000000-libre.pdf?1444807033=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Probability_of_Legal_Execu.pdf\u0026Expires=1732388432\u0026Signature=XQ41Ne8c95cXwK70sRhIAP~cRc2MMbqQ7CAj-YkqShiBny7cesPwhgzg6RgQQ39j0YClQF2AIpNeE8Faat9vOYPF56oq8wCYvJd~ynZE8aFI3sfTZhNi7qMRMRigt7wn-pWl1jNF8xD6XKiqP3exrPbgOjy4kpiti6e2PqNaw1kq7DsC91P4y7MV64Jg9Rh6dLK15yKvgctMeoC43bQE3xl0xVeK3PrPIEibkAqYz7MFbz0AfoTvUAtmDA7noYa7~n9n00hP0HmGMZ7pUqlP2Tv2xksbChidTYrwbSCspdGAXT-gep0ssQTwkQd1U0dUlqtANled-RM-FzufqJeMyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_Probability_of_Legal_Execution_of_Plans_with_Temporal_Uncertainty","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170680,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170680/thumbnails/1.jpg","file_name":"00b49520e1396c30c0000000.pdf","download_url":"https://www.academia.edu/attachments/39170680/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_Probability_of_Legal_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170680/00b49520e1396c30c0000000-libre.pdf?1444807033=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Probability_of_Legal_Execu.pdf\u0026Expires=1732388432\u0026Signature=XQ41Ne8c95cXwK70sRhIAP~cRc2MMbqQ7CAj-YkqShiBny7cesPwhgzg6RgQQ39j0YClQF2AIpNeE8Faat9vOYPF56oq8wCYvJd~ynZE8aFI3sfTZhNi7qMRMRigt7wn-pWl1jNF8xD6XKiqP3exrPbgOjy4kpiti6e2PqNaw1kq7DsC91P4y7MV64Jg9Rh6dLK15yKvgctMeoC43bQE3xl0xVeK3PrPIEibkAqYz7MFbz0AfoTvUAtmDA7noYa7~n9n00hP0HmGMZ7pUqlP2Tv2xksbChidTYrwbSCspdGAXT-gep0ssQTwkQd1U0dUlqtANled-RM-FzufqJeMyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764531"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764531/Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning"><img alt="Research paper thumbnail of Discovering and Exploiting Entailement Relationships in Multi-Label Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/39170670/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764531/Discovering_and_Exploiting_Entailement_Relationships_in_Multi_Label_Learning">Discovering and Exploiting Entailement Relationships in Multi-Label Learning</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work presents a sound probabilistic method for enforcing adherence of the marginal probabili...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailement: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented with a Bayesian network. Marginal probabilities are entered as soft evidence in the network and adjusted through probabilistic inference. Our approach offers robust improvements in mean average precision compared to the standard binary relavance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="33c86efcf8b5002b7dd562514171953c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170670,"asset_id":16764531,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170670/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764531"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764531"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764531; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764531]").text(description); $(".js-view-count[data-work-id=16764531]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764531; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764531']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764531, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "33c86efcf8b5002b7dd562514171953c" } } $('.js-work-strip[data-work-id=16764531]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764531,"title":"Discovering and Exploiting Entailement Relationships in Multi-Label Learning","translated_title":"","metadata":{"abstract":"This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. 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Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 &#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bfba3a84b436ecdcb0ef9952c4776d1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170669,"asset_id":16764530,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764530"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764530"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764530; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764530]").text(description); $(".js-view-count[data-work-id=16764530]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764530; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764530']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764530, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bfba3a84b436ecdcb0ef9952c4776d1f" } } $('.js-work-strip[data-work-id=16764530]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764530,"title":"Identifying Markov Blankets with Decision Tree Induction","translated_title":"","metadata":{"abstract":"The Markov blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 \u0026#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared."},"translated_abstract":"The Markov blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. We apply decision tree induction to the task of Markov blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0 \u0026#39;s rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.","internal_url":"https://www.academia.edu/16764530/Identifying_Markov_Blankets_with_Decision_Tree_Induction","translated_internal_url":"","created_at":"2015-10-13T23:42:10.194-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170669,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170669/thumbnails/1.jpg","file_name":"00b7d514b3a2d3247f000000.pdf","download_url":"https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Identifying_Markov_Blankets_with_Decisio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170669/00b7d514b3a2d3247f000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DIdentifying_Markov_Blankets_with_Decisio.pdf\u0026Expires=1732388432\u0026Signature=aXs8sfKw2t4RAnyOjWoAYDAqLJ6qV23J07TyGa9NBHUTJ5VLPX~X68oeSeTohs-hVGw-FlLfXWy7AEmR8p7qOpfsBwI6r~67D~sFlVWuKOkjBaP8Aa4rfnUCxqVWFeUedwq8IjHy9elAgLwq0xJo7C6MLPNek9s2usth2xoEhEhpft2ADj8tlslujpH9WhtGh2XHBKU29qoZQhgvpZ1KNtIvXUq4z0Gi7JBN-vqtpmMz~T9mQeIWd1ndnVefcxlQftRsWQSl~7wNqquOCXRPYJavpayTvynGcIouPirTkQqpDTJCPE76XxzIX69zDEC~PmfBhinSk6n97QKLCyI5HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Identifying_Markov_Blankets_with_Decision_Tree_Induction","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170669,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170669/thumbnails/1.jpg","file_name":"00b7d514b3a2d3247f000000.pdf","download_url":"https://www.academia.edu/attachments/39170669/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Identifying_Markov_Blankets_with_Decisio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170669/00b7d514b3a2d3247f000000-libre.pdf?1444807036=\u0026response-content-disposition=attachment%3B+filename%3DIdentifying_Markov_Blankets_with_Decisio.pdf\u0026Expires=1732388432\u0026Signature=aXs8sfKw2t4RAnyOjWoAYDAqLJ6qV23J07TyGa9NBHUTJ5VLPX~X68oeSeTohs-hVGw-FlLfXWy7AEmR8p7qOpfsBwI6r~67D~sFlVWuKOkjBaP8Aa4rfnUCxqVWFeUedwq8IjHy9elAgLwq0xJo7C6MLPNek9s2usth2xoEhEhpft2ADj8tlslujpH9WhtGh2XHBKU29qoZQhgvpZ1KNtIvXUq4z0Gi7JBN-vqtpmMz~T9mQeIWd1ndnVefcxlQftRsWQSl~7wNqquOCXRPYJavpayTvynGcIouPirTkQqpDTJCPE76XxzIX69zDEC~PmfBhinSk6n97QKLCyI5HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":101465,"name":"Causal Discovery","url":"https://www.academia.edu/Documents/in/Causal_Discovery"},{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network"}],"urls":[]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764528"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764528/A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty"><img alt="Research paper thumbnail of A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty" class="work-thumbnail" src="https://attachments.academia-assets.com/42402146/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764528/A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty">A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. In real domains how- ever, this assumption is commonly violated and certain events are beyond the di- rect control of the plan&amp;#x27;s executive. Previous work on reasoning with uncontrol- lable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) fol- lowing a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dy- namic schedule where scheduling decisions can be made on-line.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="782f7c4458556e3290a672e1217cfa4f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42402146,"asset_id":16764528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42402146/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764528]").text(description); $(".js-view-count[data-work-id=16764528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "782f7c4458556e3290a672e1217cfa4f" } } $('.js-work-strip[data-work-id=16764528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764528,"title":"A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty","translated_title":"","metadata":{"abstract":"In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. In real domains how- ever, this assumption is commonly violated and certain events are beyond the di- rect control of the plan\u0026amp;#x27;s executive. Previous work on reasoning with uncontrol- lable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) fol- lowing a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dy- namic schedule where scheduling decisions can be made on-line.","publication_date":{"day":null,"month":null,"year":2002,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"In Temporal Planning a typical assumption is that the agent controls the execu- tion time of all events such as starting and ending actions. In real domains how- ever, this assumption is commonly violated and certain events are beyond the di- rect control of the plan\u0026amp;#x27;s executive. Previous work on reasoning with uncontrol- lable events (Simple Temporal Problem with Uncertainty) assumes that we can bound the occurrence of each uncontrollable within a time interval. In principle however, there is no such bounding interval since there is always a non-zero probability the event will occur outside the bounds. Here we develop a new more general formalism called the Probabilistic Simple Temporal Problem (PSTP) fol- lowing a probabilistic approach. We present a method for scheduling a PSTP maximizing the probability of correct execution. Subsequently, we use this method to solve the problem of finding an optimal execution strategy, i.e. a dy- namic schedule where scheduling decisions can be made on-line.","internal_url":"https://www.academia.edu/16764528/A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty","translated_internal_url":"","created_at":"2015-10-13T23:42:09.996-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":42402146,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/42402146/thumbnails/1.jpg","file_name":"A_Probabilistic_Approach_to_Robust_Execu20160208-20925-8zc9et.pdf","download_url":"https://www.academia.edu/attachments/42402146/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Probabilistic_Approach_to_Robust_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/42402146/A_Probabilistic_Approach_to_Robust_Execu20160208-20925-8zc9et-libre.pdf?1454964410=\u0026response-content-disposition=attachment%3B+filename%3DA_Probabilistic_Approach_to_Robust_Execu.pdf\u0026Expires=1732388432\u0026Signature=TPF1w3B0hUG5IPMltcV1v4n0LFvSZKcCmtPcj6vrh4cfPfhNx2Csu-oiggvT6gCPEftVzcrdnkfRXOSGRH59tpJIxWPqJHv5VGZPMdKqzFNoZHNQZOnDCy2R13c~NIglz01c45ZJjlhPrg1gPInwv3Pz3c5ibw~mOc1LBEPF9~xb3QI0h-Wtpkq~3LvelWGh10Sw8vqLmuGKtBdKrvuaZacj1nbqlI3bZV3NuAdM3tW68drXVKU~7w~9VkaI36HuDg8eL~5B~RUb4WxU7jPJjmKX1VfjSQxggGXTtrPdOAkZAM9c1zsEMIP-ffbjDtmEJEsLKsFKEba0AEDBmsPhsw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":42402146,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/42402146/thumbnails/1.jpg","file_name":"A_Probabilistic_Approach_to_Robust_Execu20160208-20925-8zc9et.pdf","download_url":"https://www.academia.edu/attachments/42402146/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMiw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Probabilistic_Approach_to_Robust_Execu.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/42402146/A_Probabilistic_Approach_to_Robust_Execu20160208-20925-8zc9et-libre.pdf?1454964410=\u0026response-content-disposition=attachment%3B+filename%3DA_Probabilistic_Approach_to_Robust_Execu.pdf\u0026Expires=1732388432\u0026Signature=TPF1w3B0hUG5IPMltcV1v4n0LFvSZKcCmtPcj6vrh4cfPfhNx2Csu-oiggvT6gCPEftVzcrdnkfRXOSGRH59tpJIxWPqJHv5VGZPMdKqzFNoZHNQZOnDCy2R13c~NIglz01c45ZJjlhPrg1gPInwv3Pz3c5ibw~mOc1LBEPF9~xb3QI0h-Wtpkq~3LvelWGh10Sw8vqLmuGKtBdKrvuaZacj1nbqlI3bZV3NuAdM3tW68drXVKU~7w~9VkaI36HuDg8eL~5B~RUb4WxU7jPJjmKX1VfjSQxggGXTtrPdOAkZAM9c1zsEMIP-ffbjDtmEJEsLKsFKEba0AEDBmsPhsw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":6574130,"url":"https://www.researchgate.net/profile/Ioannis_Tsamardinos/publication/221238898_A_Probabilistic_Approach_to_Robust_Execution_of_Temporal_Plans_with_Uncertainty/links/02e7e51e7c5917810d000000.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764526"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764526/Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System"><img alt="Research paper thumbnail of Execution-Time Plan Management for a Cognitive Orthotic System" class="work-thumbnail" src="https://attachments.academia-assets.com/39170671/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764526/Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System">Execution-Time Plan Management for a Cognitive Orthotic System</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2002</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c3bd7da3ce672529a1dfb3caa01b6589" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39170671,"asset_id":16764526,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39170671/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764526"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764526"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764526; 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Autominder is being designed as part of an initiative on the development of robotic assistants for the elderly. Autominder stores and updates user plans, tracks their execution via input from robot sensors, and provides carefully chosen and timed reminders of the activities to be performed. It will eventually also learn the typical behavior of the user with regard to the execution of these plans. A central component of Autominder is its Plan Manager (PM), which is responsible for the temporal reasoning involved in updating plans and tracking their execution. The PM models plan update problems as disjunctive temporal problems (DTPs) and uses the Epilitis DTPsolving system to handle them. We describe the plan representations and algorithms used by the Plan Manager, and briefly discuss its connections with the rest of the system.","publication_date":{"day":null,"month":null,"year":2002,"errors":{}},"publication_name":"Lecture Notes in Computer Science","grobid_abstract_attachment_id":39170671},"translated_abstract":null,"internal_url":"https://www.academia.edu/16764526/Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System","translated_internal_url":"","created_at":"2015-10-13T23:42:09.782-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":24434052,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":39170671,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170671/thumbnails/1.jpg","file_name":"02e7e51e7c5919516a000000.pdf","download_url":"https://www.academia.edu/attachments/39170671/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Execution_Time_Plan_Management_for_a_Cog.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170671/02e7e51e7c5919516a000000-libre.pdf?1444807037=\u0026response-content-disposition=attachment%3B+filename%3DExecution_Time_Plan_Management_for_a_Cog.pdf\u0026Expires=1732388432\u0026Signature=ICkgO8i453s48UCplpiTcFDWNswjN7rTN6BIg1CSLiPBLhJGCfydrL94lhrzzYAdPLQPAku3YAibZ-QU-ZHxQKFH7oSZaKfVU9Oi-kLU1jIO12t3T3SUdcpuOCS5RgChGohMsBVwI8wHJYI6fr5VtPwGgxsebSK8u~M7eIyNAqMAtu5MyPXaL~8rAJTb9qi7CqWjnCMpaNDWPRBPD0g4VACQwVO1-z~v0GBW8xAj4hTAJUjERbKmsiDliRH32dH-77wB9NgK8WsjduP61u8pkO7Con71x~AFSdqXOnrwVCj2QZ2voTVGljnA3wmNymvlYLGRJ1-OedkqZwTXQ0Z7Lw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Execution_Time_Plan_Management_for_a_Cognitive_Orthotic_System","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":24434052,"first_name":"Ioannis","middle_initials":"","last_name":"Tsamardinos","page_name":"IoannisTsamardinos","domain_name":"crete","created_at":"2015-01-06T08:36:33.720-08:00","display_name":"Ioannis Tsamardinos","url":"https://crete.academia.edu/IoannisTsamardinos"},"attachments":[{"id":39170671,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39170671/thumbnails/1.jpg","file_name":"02e7e51e7c5919516a000000.pdf","download_url":"https://www.academia.edu/attachments/39170671/download_file?st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&st=MTczMjM4NDgzMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Execution_Time_Plan_Management_for_a_Cog.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39170671/02e7e51e7c5919516a000000-libre.pdf?1444807037=\u0026response-content-disposition=attachment%3B+filename%3DExecution_Time_Plan_Management_for_a_Cog.pdf\u0026Expires=1732388433\u0026Signature=CErTd7G9HmBY6CwUqxiOBYOX2jXCL1szKRl3YX2eNf2QnVaf1wX3ycw7L5NWre9yCQ~CEaLhm0GEoBqGNTdqD-uKwRHCEwHjBlYrHYs92dAUdFrpzfM8HDd9bDDIL~vULHj8VrkhL03o3yoIHTvAW64FeTWYlsuTaZHoAHa9LzqvVe~uOUfouY9fD6n57stN3kwXiDtAHN4FczYYT-nabq7acnHxCZgFXTBy5UcOlQGeWyL9klmG1eukJ8XA4SIjmNmz3S8glGODcyNkSlFKWeoiyHayqzaluJqfdWmZdhjBaQZB5VfVUsnyB86UAGRX7rny7sUoiYbzC10Hrmn8LA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":56484,"name":"Spatial and Temporal Reasoning","url":"https://www.academia.edu/Documents/in/Spatial_and_Temporal_Reasoning"},{"id":451955,"name":"Plan","url":"https://www.academia.edu/Documents/in/Plan"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16764525"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/16764525/MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology"><img alt="Research paper thumbnail of MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16764525/MiRduplexSVM_A_High_Performing_MiRNA_Duplex_Prediction_and_Evaluation_Methodology">MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology</a></div><div class="wp-workCard_item"><span>PLOS ONE</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16764525"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16764525"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16764525; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16764525]").text(description); $(".js-view-count[data-work-id=16764525]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16764525; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16764525']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 16764525, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16764525]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16764525,"title":"MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology","translated_title":"","metadata":{"abstract":"We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/16348963/MXM_reference_manual">MXM reference manual</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://forth.academia.edu/GiorgosAthineou">Giorgos Athineou</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/%CE%9C%CE%B9%CF%87%CE%B1%CE%AE%CE%BB%CE%A4%CF%83%CE%B1%CE%B3%CF%81%CE%AE%CF%82">螠喂蠂伪萎位韦蟽伪纬蟻萎蟼</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://crete.academia.edu/IoannisTsamardinos">Ioannis Tsamardinos</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">MXM is an R package which offers variable selection for high-dimensional data in cases of regress...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">MXM is an R package which offers variable selection for high-dimensional data in cases of regression and classification. Many regression models are offered. 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