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Tree</title> <meta name="description" content="Search results for: Decision Tree"> <meta name="keywords" content="Decision Tree"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Decision Tree"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1736</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Decision Tree</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1736</span> Improved C-Fuzzy Decision Tree for Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Krishnamoorthi%20Makkithaya">Krishnamoorthi Makkithaya</a>, <a href="https://publications.waset.org/search?q=N.%20V.%20Subba%20Reddy"> N. V. Subba Reddy</a>, <a href="https://publications.waset.org/search?q=U.%20Dinesh%20Acharya"> U. Dinesh Acharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20mining" title="Data mining">Data mining</a>, <a href="https://publications.waset.org/search?q=Decision%20tree" title=" Decision tree"> Decision tree</a>, <a href="https://publications.waset.org/search?q=Feature%20selection" title=" Feature selection"> Feature selection</a>, <a href="https://publications.waset.org/search?q=Fuzzyc-%20means%20clustering" title=" Fuzzyc- means clustering"> Fuzzyc- means clustering</a>, <a href="https://publications.waset.org/search?q=Intrusion%20detection." title=" Intrusion detection."> Intrusion detection.</a> </p> <a href="https://publications.waset.org/15151/improved-c-fuzzy-decision-tree-for-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15151/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15151/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15151/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15151/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15151/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15151/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15151/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15151/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15151/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15151/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1577</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1735</span> An Attribute-Centre Based Decision Tree Classification Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=G%C3%B6khan%20Silahtaro%C4%9Flu">G枚khan Silahtaro臒lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=split" title=" split"> split</a>, <a href="https://publications.waset.org/search?q=pruning" title=" pruning"> pruning</a>, <a href="https://publications.waset.org/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/search?q=gini." title=" gini."> gini.</a> </p> <a href="https://publications.waset.org/14986/an-attribute-centre-based-decision-tree-classification-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14986/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14986/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14986/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14986/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14986/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14986/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14986/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14986/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14986/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14986/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14986.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1370</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1734</span> Pruning Method of Belief Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Salsabil%20Trabelsi">Salsabil Trabelsi</a>, <a href="https://publications.waset.org/search?q=Zied%20Elouedi"> Zied Elouedi</a>, <a href="https://publications.waset.org/search?q=Khaled%20Mellouli"> Khaled Mellouli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/search?q=belief%20function%20theory" title=" belief function theory"> belief function theory</a>, <a href="https://publications.waset.org/search?q=belief%20decision%20tree" title=" belief decision tree"> belief decision tree</a>, <a href="https://publications.waset.org/search?q=pruning." title=" pruning."> pruning.</a> </p> <a href="https://publications.waset.org/3379/pruning-method-of-belief-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3379/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3379/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3379/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3379/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3379/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3379/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3379/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3379/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3379/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3379/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1910</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1733</span> Decision Tree-based Feature Ranking using Manhattan Hierarchical Cluster Criterion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yasmin%20Mohd%20Yacob">Yasmin Mohd Yacob</a>, <a href="https://publications.waset.org/search?q=Harsa%20A.%20Mat%20Sakim"> Harsa A. Mat Sakim</a>, <a href="https://publications.waset.org/search?q=Nor%20Ashidi%20Mat%20Isa"> Nor Ashidi Mat Isa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used decision tree as a feature ranker with a direct threshold measure, while others remain the decision tree but utilized pruning condition that act as a threshold mechanism to choose features. This paper proposed threshold measure using Manhattan Hierarchical Cluster distance to be utilized in feature ranking in order to choose relevant features as part of the feature selection process. The result is promising, and this method can be improved in the future by including test cases of a higher number of attributes.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Feature%20ranking" title="Feature ranking">Feature ranking</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=hierarchical%20cluster" title=" hierarchical cluster"> hierarchical cluster</a>, <a href="https://publications.waset.org/search?q=Manhattan%20distance." title=" Manhattan distance."> Manhattan distance.</a> </p> <a href="https://publications.waset.org/1993/decision-tree-based-feature-ranking-using-manhattan-hierarchical-cluster-criterion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1993/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1993/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1993/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1993/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1993/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1993/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1993/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1993/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1993/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1993/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1969</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1732</span> Comparison of Experimental Relationships to Determine Flow Discharge in Meandering Compound Channels Using M5 Decision Tree Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mehdi%20Kheradmand">Mehdi Kheradmand</a>, <a href="https://publications.waset.org/search?q=Mehdi%20Azhdary%20Moghaddam"> Mehdi Azhdary Moghaddam</a>, <a href="https://publications.waset.org/search?q=Abdolreza%20Zahiri"> Abdolreza Zahiri</a>, <a href="https://publications.waset.org/search?q=Khalil%20Ghorbani"> Khalil Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research compares results of major methods of determining the flow discharge using experimental relationships with results from the M5 decision tree model in meandering compound sections in several laboratory channels. It was found that the M5 decision tree model enjoyed greater accuracy of statistical parameters compared to methods to the said methods. This suggested that the M5 decision tree model has highly improved the calculated accuracy of the flow discharge in meandering compound channels. </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Stage-discharge%20relationship" title="Stage-discharge relationship">Stage-discharge relationship</a>, <a href="https://publications.waset.org/search?q=M5%20decision%20tree%20model" title=" M5 decision tree model"> M5 decision tree model</a>, <a href="https://publications.waset.org/search?q=compound%20section" title=" compound section"> compound section</a>, <a href="https://publications.waset.org/search?q=meandering%20compound%20channel." title=" meandering compound channel."> meandering compound channel.</a> </p> <a href="https://publications.waset.org/10013189/comparison-of-experimental-relationships-to-determine-flow-discharge-in-meandering-compound-channels-using-m5-decision-tree-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013189/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013189/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013189/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013189/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013189/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013189/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013189/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013189/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013189/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013189/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">231</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1731</span> Decision Tree Based Scheduling for Flexible Job Shops with Multiple Process Plans </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.-H.%20Doh">H.-H. Doh</a>, <a href="https://publications.waset.org/search?q=J.-M.%20Yu"> J.-M. Yu</a>, <a href="https://publications.waset.org/search?q=Y.-J.%20Kwon"> Y.-J. Kwon</a>, <a href="https://publications.waset.org/search?q=J.-H.%20Shin"> J.-H. Shin</a>, <a href="https://publications.waset.org/search?q=H.-W.%20Kim"> H.-W. Kim</a>, <a href="https://publications.waset.org/search?q=S.-H.%20Nam"> S.-H. Nam</a>, <a href="https://publications.waset.org/search?q=D.-H.%20Lee"> D.-H. Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Flexible%20job%20shop%20scheduling" title="Flexible job shop scheduling">Flexible job shop scheduling</a>, <a href="https://publications.waset.org/search?q=Decision%20tree" title=" Decision tree"> Decision tree</a>, <a href="https://publications.waset.org/search?q=Priority%20rules" title=" Priority rules"> Priority rules</a>, <a href="https://publications.waset.org/search?q=Case%20study." title=" Case study. "> Case study. </a> </p> <a href="https://publications.waset.org/9997827/decision-tree-based-scheduling-for-flexible-job-shops-with-multiple-process-plans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997827/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997827/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997827/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997827/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997827/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997827/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997827/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997827/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997827/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997827/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997827.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3319</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1730</span> Defect Cause Modeling with Decision Tree and Regression Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=B.%20Bak%C4%B1r">B. Bak谋r</a>, <a href="https://publications.waset.org/search?q=%C4%B0.%20Batmaz"> 陌. Batmaz</a>, <a href="https://publications.waset.org/search?q=F.%20A.%20G%C3%BCnt%C3%BCrk%C3%BCn"> F. A. G眉nt眉rk眉n</a>, <a href="https://publications.waset.org/search?q=%C4%B0.%20A.%20%C4%B0pek%C3%A7i"> 陌. A. 陌pek莽i</a>, <a href="https://publications.waset.org/search?q=G.%20K%C3%B6ksal"> G. K枚ksal</a>, <a href="https://publications.waset.org/search?q=N.%20E.%20%C3%96zdemirel"> N. E. 脰zdemirel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision treesare used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Casting%20industry" title="Casting industry">Casting industry</a>, <a href="https://publications.waset.org/search?q=decision%20tree%20algorithm%20C5.0" title=" decision tree algorithm C5.0"> decision tree algorithm C5.0</a>, <a href="https://publications.waset.org/search?q=logistic%20regression" title="logistic regression">logistic regression</a>, <a href="https://publications.waset.org/search?q=quality%20improvement." title=" quality improvement."> quality improvement.</a> </p> <a href="https://publications.waset.org/9221/defect-cause-modeling-with-decision-tree-and-regression-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9221/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9221/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9221/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9221/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9221/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9221/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9221/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9221/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9221/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9221/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2520</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1729</span> Determining of Stage-Discharge Relationship for Meandering Compound Channels Using M5 Decision Tree Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mehdi%20Kheradmand">Mehdi Kheradmand</a>, <a href="https://publications.waset.org/search?q=Mehdi%20Azhdary%20Moghaddam"> Mehdi Azhdary Moghaddam</a>, <a href="https://publications.waset.org/search?q=Abdolreza%20Zahiri"> Abdolreza Zahiri</a>, <a href="https://publications.waset.org/search?q=Khalil%20Ghorbani"> Khalil Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In modeling phenomena, the presence of local conditions may cause the use of a general relation not to produce good results and thus fail to demonstrate local changes. If possible, identifying homogenous limits and providing simple linear relations for each of these limits will increase the accuracy of models. Accordingly, the models are divided into simpler and smaller problems to solve complicated problems, and the obtained answers will be combined. This simple idea can be applied to decision tree models. For this aim, the input data values are divided into several sub-intervals or sub-regions, and an appropriate model is extracted for an appropriate model or equation. This research proposes the M5 decision tree method as a solution to accurately compute the flow discharge in meandering compound channels.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Stage-discharge%20relationship" title="Stage-discharge relationship">Stage-discharge relationship</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=M5%20decision%20tree%20model" title=" M5 decision tree model"> M5 decision tree model</a>, <a href="https://publications.waset.org/search?q=meandering%20compound%20channels." title=" meandering compound channels."> meandering compound channels.</a> </p> <a href="https://publications.waset.org/10013165/determining-of-stage-discharge-relationship-for-meandering-compound-channels-using-m5-decision-tree-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013165/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013165/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013165/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013165/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013165/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013165/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013165/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013165/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013165/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013165/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">246</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1728</span> A Comparison of Single of Decision Tree, Decision Tree Forest and Group Method of Data Handling to Evaluate the Surface Roughness in Machining Process </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Decision%20Tree%20Forest" title="Decision Tree Forest">Decision Tree Forest</a>, <a href="https://publications.waset.org/search?q=GMDH" title=" GMDH"> GMDH</a>, <a href="https://publications.waset.org/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/search?q=taguchi%20method" title=" taguchi method"> taguchi method</a>, <a href="https://publications.waset.org/search?q=turning%20process." title=" turning process."> turning process.</a> </p> <a href="https://publications.waset.org/10006379/a-comparison-of-single-of-decision-tree-decision-tree-forest-and-group-method-of-data-handling-to-evaluate-the-surface-roughness-in-machining-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006379/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006379/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006379/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006379/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006379/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006379/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006379/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006379/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006379/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006379/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">955</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1727</span> Ensemble Learning with Decision Tree for Remote Sensing Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mahesh%20Pal">Mahesh Pal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, a number of works proposing the combination of multiple classifiers to produce a single classification have been reported in remote sensing literature. The resulting classifier, referred to as an ensemble classifier, is generally found to be more accurate than any of the individual classifiers making up the ensemble. As accuracy is the primary concern, much of the research in the field of land cover classification is focused on improving classification accuracy. This study compares the performance of four ensemble approaches (boosting, bagging, DECORATE and random subspace) with a univariate decision tree as base classifier. Two training datasets, one without ant noise and other with 20 percent noise was used to judge the performance of different ensemble approaches. Results with noise free data set suggest an improvement of about 4% in classification accuracy with all ensemble approaches in comparison to the results provided by univariate decision tree classifier. Highest classification accuracy of 87.43% was achieved by boosted decision tree. A comparison of results with noisy data set suggests that bagging, DECORATE and random subspace approaches works well with this data whereas the performance of boosted decision tree degrades and a classification accuracy of 79.7% is achieved which is even lower than that is achieved (i.e. 80.02%) by using unboosted decision tree classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Ensemble%20learning" title="Ensemble learning">Ensemble learning</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=remote%20sensingclassification." title=" remote sensingclassification."> remote sensingclassification.</a> </p> <a href="https://publications.waset.org/717/ensemble-learning-with-decision-tree-for-remote-sensing-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/717/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/717/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/717/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/717/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/717/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/717/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/717/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/717/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/717/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/717/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/717.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2584</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1726</span> Decision Tree for Competing Risks Survival Probability in Breast Cancer Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=N.%20A.%20Ibrahim">N. A. Ibrahim</a>, <a href="https://publications.waset.org/search?q=A.%20Kudus"> A. Kudus</a>, <a href="https://publications.waset.org/search?q=I.%20Daud"> I. Daud</a>, <a href="https://publications.waset.org/search?q=M.%20R.%20Abu%20Bakar"> M. R. Abu Bakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Competing risks survival data that comprises of more than one type of event has been used in many applications, and one of these is in clinical study (e.g. in breast cancer study). The decision tree method can be extended to competing risks survival data by modifying the split function so as to accommodate two or more risks which might be dependent on each other. Recently, researchers have constructed some decision trees for recurrent survival time data using frailty and marginal modelling. We further extended the method for the case of competing risks. In this paper, we developed the decision tree method for competing risks survival time data based on proportional hazards for subdistribution of competing risks. In particular, we grow a tree by using deviance statistic. The application of breast cancer data is presented. Finally, to investigate the performance of the proposed method, simulation studies on identification of true group of observations were executed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Competing%20risks" title="Competing risks">Competing risks</a>, <a href="https://publications.waset.org/search?q=Decision%20tree" title=" Decision tree"> Decision tree</a>, <a href="https://publications.waset.org/search?q=Simulation" title=" Simulation"> Simulation</a>, <a href="https://publications.waset.org/search?q=Subdistribution%20Proportional%20Hazard." title="Subdistribution Proportional Hazard.">Subdistribution Proportional Hazard.</a> </p> <a href="https://publications.waset.org/12319/decision-tree-for-competing-risks-survival-probability-in-breast-cancer-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12319/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12319/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12319/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12319/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12319/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12319/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12319/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12319/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12319/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12319/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2374</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1725</span> An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cutting%20condition" title="Cutting condition">Cutting condition</a>, <a href="https://publications.waset.org/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=CART%20algorithm." title=" CART algorithm."> CART algorithm.</a> </p> <a href="https://publications.waset.org/10007017/an-alternative-approach-for-assessing-the-impact-of-cutting-conditions-on-surface-roughness-using-single-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007017/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007017/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007017/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007017/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007017/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007017/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007017/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007017/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007017/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007017/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">869</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1724</span> Parametric and Nonparametric Analysis of Breast Cancer Treatments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chunling%20Cong">Chunling Cong</a>, <a href="https://publications.waset.org/search?q=Chris.P.Tsokos"> Chris.P.Tsokos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of the present research manuscript is to perform parametric, nonparametric, and decision tree analysis to evaluate two treatments that are being used for breast cancer patients. Our study is based on utilizing real data which was initially used in 鈥淭amoxifen with or without breast irradiation in women of 50 years of age or older with early breast cancer" [1], and the data is supplied to us by N.A. Ibrahim 鈥淒ecision tree for competing risks survival probability in breast cancer study" [2]. We agree upon certain aspects of our findings with the published results. However, in this manuscript, we focus on relapse time of breast cancer patients instead of survival time and parametric analysis instead of semi-parametric decision tree analysis is applied to provide more precise recommendations of effectiveness of the two treatments with respect to reoccurrence of breast cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/search?q=breast%20cancer%20treatments" title=" breast cancer treatments"> breast cancer treatments</a>, <a href="https://publications.waset.org/search?q=parametricanalysis" title=" parametricanalysis"> parametricanalysis</a>, <a href="https://publications.waset.org/search?q=non-parametric%20analysis" title=" non-parametric analysis"> non-parametric analysis</a> </p> <a href="https://publications.waset.org/1207/parametric-and-nonparametric-analysis-of-breast-cancer-treatments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1207/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1207/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1207/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1207/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1207/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1207/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1207/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1207/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1207/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1207/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2052</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1723</span> Learning User Keystroke Patterns for Authentication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ying%20Zhao">Ying Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keystroke authentication is a new access control system to identify legitimate users via their typing behavior. In this paper, machine learning techniques are adapted for keystroke authentication. Seven learning methods are used to build models to differentiate user keystroke patterns. The selected classification methods are Decision Tree, Naive Bayesian, Instance Based Learning, Decision Table, One Rule, Random Tree and K-star. Among these methods, three of them are studied in more details. The results show that machine learning is a feasible alternative for keystroke authentication. Compared to the conventional Nearest Neighbour method in the recent research, learning methods especially Decision Tree can be more accurate. In addition, the experiment results reveal that 3-Grams is more accurate than 2-Grams and 4-Grams for feature extraction. Also, combination of attributes tend to result higher accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Keystroke%20Authentication" title="Keystroke Authentication">Keystroke Authentication</a>, <a href="https://publications.waset.org/search?q=Pattern%20recognition" title=" Pattern recognition"> Pattern recognition</a>, <a href="https://publications.waset.org/search?q=MachineLearning" title=" MachineLearning"> MachineLearning</a>, <a href="https://publications.waset.org/search?q=Instance-based%20Learning" title=" Instance-based Learning"> Instance-based Learning</a>, <a href="https://publications.waset.org/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/search?q=Decision%20Tree." title=" Decision Tree."> Decision Tree.</a> </p> <a href="https://publications.waset.org/9888/learning-user-keystroke-patterns-for-authentication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9888/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9888/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9888/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9888/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9888/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9888/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9888/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9888/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9888/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9888/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2822</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1722</span> Predicting Protein Function using Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Manpreet%20Singh">Manpreet Singh</a>, <a href="https://publications.waset.org/search?q=Parminder%20Kaur%20Wadhwa"> Parminder Kaur Wadhwa</a>, <a href="https://publications.waset.org/search?q=Surinder%20Kaur"> Surinder Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The drug discovery process starts with protein identification because proteins are responsible for many functions required for maintenance of life. Protein identification further needs determination of protein function. Proposed method develops a classifier for human protein function prediction. The model uses decision tree for classification process. The protein function is predicted on the basis of matched sequence derived features per each protein function. The research work includes the development of a tool which determines sequence derived features by analyzing different parameters. The other sequence derived features are determined using various web based tools. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Sequence%20Derived%20Features" title="Sequence Derived Features">Sequence Derived Features</a>, <a href="https://publications.waset.org/search?q=decision%20tree." title=" decision tree."> decision tree.</a> </p> <a href="https://publications.waset.org/9917/predicting-protein-function-using-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9917/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9917/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9917/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9917/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9917/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9917/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9917/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9917/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9917/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9917/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1951</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1721</span> Decision Tree Modeling in Emergency Logistics Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yousef%20Abu%20Nahleh">Yousef Abu Nahleh</a>, <a href="https://publications.waset.org/search?q=Arun%20Kumar"> Arun Kumar</a>, <a href="https://publications.waset.org/search?q=Fugen%20Daver"> Fugen Daver</a>, <a href="https://publications.waset.org/search?q=Reham%20Al-Hindawi"> Reham Al-Hindawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Decision%20tree%20modeling" title="Decision tree modeling">Decision tree modeling</a>, <a href="https://publications.waset.org/search?q=Forecasting" title=" Forecasting"> Forecasting</a>, <a href="https://publications.waset.org/search?q=Humanitarian%20relief" title=" Humanitarian relief"> Humanitarian relief</a>, <a href="https://publications.waset.org/search?q=emergency%20supply%20chain." title=" emergency supply chain."> emergency supply chain.</a> </p> <a href="https://publications.waset.org/9998488/decision-tree-modeling-in-emergency-logistics-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9998488/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9998488/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9998488/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9998488/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9998488/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9998488/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9998488/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9998488/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9998488/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9998488/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9998488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3307</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1720</span> Scaling up Detection Rates and Reducing False Positives in Intrusion Detection using NBTree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dewan%20Md.%20Farid">Dewan Md. Farid</a>, <a href="https://publications.waset.org/search?q=Nguyen%20Huu%20Hoa"> Nguyen Huu Hoa</a>, <a href="https://publications.waset.org/search?q=Jerome%20Darmont"> Jerome Darmont</a>, <a href="https://publications.waset.org/search?q=Nouria%20Harbi"> Nouria Harbi</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Zahidur%20Rahman"> Mohammad Zahidur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive na茂ve Bayesian tree (NBTree), which induces a hybrid of decision tree and na茂ve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of na茂ve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that na茂ve Bayesian tree improves the classification rates in large dataset. In na茂ve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain na茂ve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Detection%20rates" title="Detection rates">Detection rates</a>, <a href="https://publications.waset.org/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/search?q=network%20intrusiondetection" title=" network intrusiondetection"> network intrusiondetection</a>, <a href="https://publications.waset.org/search?q=na%C3%AFve%20Bayesian%20tree." title=" na茂ve Bayesian tree."> na茂ve Bayesian tree.</a> </p> <a href="https://publications.waset.org/1750/scaling-up-detection-rates-and-reducing-false-positives-in-intrusion-detection-using-nbtree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1750/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1750/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1750/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1750/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1750/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1750/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1750/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1750/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1750/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1750/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2281</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1719</span> A Case-Based Reasoning-Decision Tree Hybrid System for Stock Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yaojun%20Wang">Yaojun Wang</a>, <a href="https://publications.waset.org/search?q=Yaoqing%20Wang"> Yaoqing Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Case-based%20reasoning" title="Case-based reasoning">Case-based reasoning</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=stock%20selection" title=" stock selection"> stock selection</a>, <a href="https://publications.waset.org/search?q=machine%20learning." title=" machine learning."> machine learning.</a> </p> <a href="https://publications.waset.org/10005355/a-case-based-reasoning-decision-tree-hybrid-system-for-stock-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005355/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005355/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005355/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005355/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005355/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005355/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005355/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005355/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005355/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005355/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005355.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1705</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1718</span> Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/search?q=B.%20Attah"> B. Attah</a>, <a href="https://publications.waset.org/search?q=S.%20Misra"> S. Misra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient鈥檚 health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20network" title="Artificial neural network">Artificial neural network</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=decision%0D%0Atree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=diabetes%20mellitus." title=" diabetes mellitus."> diabetes mellitus.</a> </p> <a href="https://publications.waset.org/10002071/performance-analysis-of-artificial-neural-network-with-decision-tree-in-prediction-of-diabetes-mellitus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002071/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002071/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002071/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002071/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002071/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002071/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002071/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002071/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002071/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002071/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002071.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2417</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1717</span> Integrating Decision Tree and Spatial Cluster Analysis for Landslide Susceptibility Zonation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chien-Min%20Chu">Chien-Min Chu</a>, <a href="https://publications.waset.org/search?q=Bor-Wen%20Tsai"> Bor-Wen Tsai</a>, <a href="https://publications.waset.org/search?q=Kang-Tsung%20Chang"> Kang-Tsung Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landslide susceptibility map delineates the potential zones for landslide occurrence. Previous works have applied multivariate methods and neural networks for mapping landslide susceptibility. This study proposed a new approach to integrate decision tree model and spatial cluster statistic for assessing landslide susceptibility spatially. A total of 2057 landslide cells were digitized for developing the landslide decision tree model. The relationships of landslides and instability factors were explicitly represented by using tree graphs in the model. The local Getis-Ord statistics were used to cluster cells with high landslide probability. The analytic result from the local Getis-Ord statistics was classed to create a map of landslide susceptibility zones. The map was validated using new landslide data with 482 cells. Results of validation show an accuracy rate of 86.1% in predicting new landslide occurrence. This indicates that the proposed approach is useful for improving landslide susceptibility mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Landslide%20susceptibility%20Zonation" title="Landslide susceptibility Zonation">Landslide susceptibility Zonation</a>, <a href="https://publications.waset.org/search?q=Decision%20treemodel" title=" Decision treemodel"> Decision treemodel</a>, <a href="https://publications.waset.org/search?q=Spatial%20cluster" title=" Spatial cluster"> Spatial cluster</a>, <a href="https://publications.waset.org/search?q=Local%20Getis-Ord%20statistics." title=" Local Getis-Ord statistics."> Local Getis-Ord statistics.</a> </p> <a href="https://publications.waset.org/11541/integrating-decision-tree-and-spatial-cluster-analysis-for-landslide-susceptibility-zonation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11541/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11541/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11541/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11541/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11541/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11541/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11541/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11541/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11541/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11541/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11541.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1941</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1716</span> Sequence-based Prediction of Gamma-turn Types using a Physicochemical Property-based Decision Tree Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chyn%20Liaw">Chyn Liaw</a>, <a href="https://publications.waset.org/search?q=Chun-Wei%20Tung"> Chun-Wei Tung</a>, <a href="https://publications.waset.org/search?q=Shinn-Jang%20Ho"> Shinn-Jang Ho</a>, <a href="https://publications.waset.org/search?q=Shinn-Ying%20Ho"> Shinn-Ying Ho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 纬-turns play important roles in protein folding and molecular recognition. The prediction and analysis of 纬-turn types are important for both protein structure predictions and better understanding the characteristics of different 纬-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict 纬-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding 纬-turn types. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification%20and%20regression%20tree%20%28CART%29" title="Classification and regression tree (CART)">Classification and regression tree (CART)</a>, <a href="https://publications.waset.org/search?q=%CE%B3-turn" title=" 纬-turn"> 纬-turn</a>, <a href="https://publications.waset.org/search?q=Physicochemical%20properties" title="Physicochemical properties">Physicochemical properties</a>, <a href="https://publications.waset.org/search?q=Protein%20secondary%20structure." title=" Protein secondary structure."> Protein secondary structure.</a> </p> <a href="https://publications.waset.org/3950/sequence-based-prediction-of-gamma-turn-types-using-a-physicochemical-property-based-decision-tree-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3950/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3950/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3950/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3950/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3950/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3950/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3950/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3950/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3950/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3950/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3950.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1551</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1715</span> Comparison among Various Question Generations for Decision Tree Based State Tying in Persian Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nasibeh%20Nasiri">Nasibeh Nasiri</a>, <a href="https://publications.waset.org/search?q=Dawood%20Talebi%20Khanmiri"> Dawood Talebi Khanmiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Performance of any continuous speech recognition system is highly dependent on performance of the acoustic models. Generally, development of the robust spoken language technology relies on the availability of large amounts of data. Common way to cope with little data for training each state of Markov models is treebased state tying. This tying method applies contextual questions to tie states. Manual procedure for question generation suffers from human errors and is time consuming. Various automatically generated questions are used to construct decision tree. There are three approaches to generate questions to construct HMMs based on decision tree. One approach is based on misrecognized phonemes, another approach basically uses feature table and the other is based on state distributions corresponding to context-independent subword units. In this paper, all these methods of automatic question generation are applied to the decision tree on FARSDAT corpus in Persian language and their results are compared with those of manually generated questions. The results show that automatically generated questions yield much better results and can replace manually generated questions in Persian language.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Decision%20Tree" title="Decision Tree">Decision Tree</a>, <a href="https://publications.waset.org/search?q=Markov%20Models" title=" Markov Models"> Markov Models</a>, <a href="https://publications.waset.org/search?q=Speech%0D%0ARecognition" title=" Speech Recognition"> Speech Recognition</a>, <a href="https://publications.waset.org/search?q=State%20Tying." title=" State Tying."> State Tying.</a> </p> <a href="https://publications.waset.org/11978/comparison-among-various-question-generations-for-decision-tree-based-state-tying-in-persian-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11978/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11978/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11978/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11978/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11978/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11978/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11978/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11978/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11978/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11978/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11978.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1722</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1714</span> Optimizing Mobile Agents Migration Based on Decision Tree Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yasser%20k.%20Ali">Yasser k. Ali</a>, <a href="https://publications.waset.org/search?q=Hesham%20N.%20Elmahdy"> Hesham N. Elmahdy</a>, <a href="https://publications.waset.org/search?q=Sanaa%20El%20Olla%20Hanfy%20Ahmed"> Sanaa El Olla Hanfy Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Agent%20Migration" title="Agent Migration">Agent Migration</a>, <a href="https://publications.waset.org/search?q=Decision%20Tree%20learning" title=" Decision Tree learning"> Decision Tree learning</a>, <a href="https://publications.waset.org/search?q=ID3%0D%0Aalgorithm" title=" ID3 algorithm"> ID3 algorithm</a>, <a href="https://publications.waset.org/search?q=Naive%20Bayes%20Classifier" title=" Naive Bayes Classifier"> Naive Bayes Classifier</a> </p> <a href="https://publications.waset.org/14719/optimizing-mobile-agents-migration-based-on-decision-tree-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14719/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14719/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14719/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14719/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14719/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14719/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14719/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14719/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14719/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14719/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14719.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1991</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1713</span> Using Data Mining Technique for Scholarship Disbursement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/search?q=S.%20A.%20Lawal"> S. A. Lawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Decision%20tree" title="Decision tree">Decision tree</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/search?q=scholarship." title=" scholarship."> scholarship.</a> </p> <a href="https://publications.waset.org/10002271/using-data-mining-technique-for-scholarship-disbursement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002271/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002271/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002271/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002271/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002271/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002271/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002271/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002271/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002271/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002271/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002271.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2158</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1712</span> Determination of the Bank's Customer Risk Profile: Data Mining Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Taner%20Ersoz">Taner Ersoz</a>, <a href="https://publications.waset.org/search?q=Filiz%20Ersoz"> Filiz Ersoz</a>, <a href="https://publications.waset.org/search?q=Seyma%20Ozbilge"> Seyma Ozbilge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Client%20classification" title="Client classification">Client classification</a>, <a href="https://publications.waset.org/search?q=loan%20suitability" title=" loan suitability"> loan suitability</a>, <a href="https://publications.waset.org/search?q=risk%20rating" title=" risk rating"> risk rating</a>, <a href="https://publications.waset.org/search?q=CART%20analysis" title=" CART analysis"> CART analysis</a>, <a href="https://publications.waset.org/search?q=decision%20tree." title=" decision tree."> decision tree.</a> </p> <a href="https://publications.waset.org/10005338/determination-of-the-banks-customer-risk-profile-data-mining-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005338/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005338/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005338/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005338/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005338/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005338/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005338/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005338/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005338/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005338/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005338.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1075</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1711</span> A Hybrid Classification Method using Artificial Neural Network Based Decision Tree for Automatic Sleep Scoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Haoyu%20Ma">Haoyu Ma</a>, <a href="https://publications.waset.org/search?q=Bin%20Hu"> Bin Hu</a>, <a href="https://publications.waset.org/search?q=Mike%20Jackson"> Mike Jackson</a>, <a href="https://publications.waset.org/search?q=Jingzhi%20Yan"> Jingzhi Yan</a>, <a href="https://publications.waset.org/search?q=Wen%20Zhao"> Wen Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Sleep" title="Sleep">Sleep</a>, <a href="https://publications.waset.org/search?q=Sleep%20stage" title=" Sleep stage"> Sleep stage</a>, <a href="https://publications.waset.org/search?q=Automatic%20sleep%20scoring" title=" Automatic sleep scoring"> Automatic sleep scoring</a>, <a href="https://publications.waset.org/search?q=Electroencephalography" title="Electroencephalography">Electroencephalography</a>, <a href="https://publications.waset.org/search?q=Decision%20tree" title=" Decision tree"> Decision tree</a>, <a href="https://publications.waset.org/search?q=Artificial%20neural%20network" title=" Artificial neural network"> Artificial neural network</a> </p> <a href="https://publications.waset.org/15945/a-hybrid-classification-method-using-artificial-neural-network-based-decision-tree-for-automatic-sleep-scoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15945/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15945/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15945/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15945/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15945/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15945/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15945/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15945/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15945/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15945/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2072</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1710</span> Hybrid Machine Learning Approach for Text Categorization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nerijus%20Remeikis">Nerijus Remeikis</a>, <a href="https://publications.waset.org/search?q=Ignas%20Skucas"> Ignas Skucas</a>, <a href="https://publications.waset.org/search?q=Vida%20Melninkaite"> Vida Melninkaite</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Text%20categorization" title="Text categorization">Text categorization</a>, <a href="https://publications.waset.org/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/search?q=machine%20learning." title=" machine learning."> machine learning.</a> </p> <a href="https://publications.waset.org/9621/hybrid-machine-learning-approach-for-text-categorization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9621/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9621/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9621/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9621/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9621/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9621/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9621/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9621/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9621/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9621/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1806</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1709</span> Association Rule and Decision Tree based Methodsfor Fuzzy Rule Base Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ferenc%20Peter%20Pach">Ferenc Peter Pach</a>, <a href="https://publications.waset.org/search?q=Janos%20Abonyi"> Janos Abonyi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the a priori fuzzy clustering based partitioning of the continuous input variables. An application study is also presented, where the developed methods are tested on the well known Wisconsin Breast Cancer classification problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=" title=""></a> </p> <a href="https://publications.waset.org/13993/association-rule-and-decision-tree-based-methodsfor-fuzzy-rule-base-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13993/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13993/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13993/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13993/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13993/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13993/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13993/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13993/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13993/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13993/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2304</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1708</span> Using Single Decision Tree to Assess the Impact of Cutting Conditions on Vibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cutting%20condition" title="Cutting condition">Cutting condition</a>, <a href="https://publications.waset.org/search?q=vibration" title=" vibration"> vibration</a>, <a href="https://publications.waset.org/search?q=natural%20frequency" title=" natural frequency"> natural frequency</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=CART%20algorithm." title=" CART algorithm."> CART algorithm.</a> </p> <a href="https://publications.waset.org/10005430/using-single-decision-tree-to-assess-the-impact-of-cutting-conditions-on-vibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005430/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005430/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005430/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005430/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005430/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005430/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005430/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005430/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005430/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005430/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1434</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1707</span> Empirical and Indian Automotive Equity Portfolio Decision Support</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=P.%20Sankar">P. Sankar</a>, <a href="https://publications.waset.org/search?q=P.%20James%20Daniel%20Paul"> P. James Daniel Paul</a>, <a href="https://publications.waset.org/search?q=Siddhant%20Sahu"> Siddhant Sahu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Indian%20Automotive%20Sector" title="Indian Automotive Sector">Indian Automotive Sector</a>, <a href="https://publications.waset.org/search?q=Stock%20Market%20Decisions" title=" Stock Market Decisions"> Stock Market Decisions</a>, <a href="https://publications.waset.org/search?q=Equity%20Portfolio%20Analysis" title=" Equity Portfolio Analysis"> Equity Portfolio Analysis</a>, <a href="https://publications.waset.org/search?q=Decision%20Tree%20Classifiers" title=" Decision Tree Classifiers"> Decision Tree Classifiers</a>, <a href="https://publications.waset.org/search?q=Statistical%20Data%20Analysis." title=" Statistical Data Analysis."> Statistical Data Analysis.</a> </p> <a 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