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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13326">arXiv:2411.13326</a> <span> [<a href="https://arxiv.org/pdf/2411.13326">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> An Evolutional Neural Network Framework for Classification of Microarray Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Evari%2C+M+E">Maryam Eshraghi Evari</a>, <a href="/search/cs?searchtype=author&query=Sulaiman%2C+M+N">Md Nasir Sulaiman</a>, <a href="/search/cs?searchtype=author&query=Behjat%2C+A+R">Amir Rajabi Behjat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13326v1-abstract-short" style="display: inline;"> DNA microarray gene-expression data has been widely used to identify cancerous gene signatures. Microarray can increase the accuracy of cancer diagnosis and prognosis. However, analyzing the large amount of gene expression data from microarray chips pose a challenge for current machine learning researches. One of the challenges lie within classification of healthy and cancerous tissues is high dim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13326v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13326v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13326v1-abstract-full" style="display: none;"> DNA microarray gene-expression data has been widely used to identify cancerous gene signatures. Microarray can increase the accuracy of cancer diagnosis and prognosis. However, analyzing the large amount of gene expression data from microarray chips pose a challenge for current machine learning researches. One of the challenges lie within classification of healthy and cancerous tissues is high dimensionality of gene expressions. High dimensionality decreases the accuracy of the classification. This research aims to apply a hybrid model of Genetic Algorithm and Neural Network to overcome the problem during subset selection of informative genes. Whereby, a Genetic Algorithm (GA) reduced dimensionality during feature selection and then a Multi-Layer perceptron Neural Network (MLP) is applied to classify selected genes. The performance evaluated by considering to the accuracy and the number of selected genes. Experimental results show the proposed method suggested high accuracy and minimum number of selected genes in comparison with other machine learning algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13326v1-abstract-full').style.display = 'none'; document.getElementById('2411.13326v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1001.2275">arXiv:1001.2275</a> <span> [<a href="https://arxiv.org/pdf/1001.2275">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Efficient Candidacy Reduction For Frequent Pattern Mining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shahraki%2C+M+N">Mohammad Nadimi Shahraki</a>, <a href="/search/cs?searchtype=author&query=Mustapha%2C+N">Norwati Mustapha</a>, <a href="/search/cs?searchtype=author&query=Sulaiman%2C+M+N+B">Md Nasir B Sulaiman</a>, <a href="/search/cs?searchtype=author&query=Mamat%2C+A+B">Ali B Mamat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1001.2275v1-abstract-short" style="display: inline;"> Certainly, nowadays knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is a main step in knowledge discovery process. Meanwhile frequent patterns play central role in data mining tasks such as clustering, classification, and association analysis. Identifying all frequent patterns is the most time consuming process due… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1001.2275v1-abstract-full').style.display = 'inline'; document.getElementById('1001.2275v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1001.2275v1-abstract-full" style="display: none;"> Certainly, nowadays knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is a main step in knowledge discovery process. Meanwhile frequent patterns play central role in data mining tasks such as clustering, classification, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of candidate patterns. For the past decade there have been an increasing number of efficient algorithms to mine the frequent patterns. However reducing the number of candidate patterns and comparisons for support counting are still two problems in this field which have made the frequent pattern mining one of the active research themes in data mining. A reasonable solution is identifying a small candidate pattern set from which can generate all frequent patterns. In this paper, a method is proposed based on a new candidate set called candidate head set or H which forms a small set of candidate patterns. The experimental results verify the accuracy of the proposed method and reduction of the number of candidate patterns and comparisons. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1001.2275v1-abstract-full').style.display = 'none'; document.getElementById('1001.2275v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 January, 2010; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2010. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS December 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis/</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> Volume 6, No. 3, ISSN 1947 5500 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 3, pp. 230-237, December 2009, USA </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" 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