<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462018000200675</article-id>
<article-id pub-id-type="doi">10.13053/cys-22-2-2819</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[SVM-RFE-ED: A Novel SVM-RFE based on Energy Distance for Gene Selection and Cancer Diagnosis]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Medjahed]]></surname>
<given-names><![CDATA[Seyyid Ahmed]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ouali]]></surname>
<given-names><![CDATA[Mohammed]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centre Universitaire Ahmed Zabana Relizane  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Algeria</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Université des Sciences et de la Technologie d&#8217;Oran Mohamed Boudiaf  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Algeria</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Taif University College of Computers and Information Technology ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Kingdom of Saudi Arabia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<volume>22</volume>
<numero>2</numero>
<fpage>675</fpage>
<lpage>683</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462018000200675&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462018000200675&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462018000200675&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Microarray expression data has been a very active research field and an indispensable tool for cancer diagnosis. The microarray expression dataset contains thousands of genes and selecting a subset of informative genes is a primordial preprocessing step for improving the cancer classification. Support Vector Machine Recursive Feature Elimination (SVM-RFE) is one of the popular and effective gene selection approaches. However, SVM-RFE attempts to find the best possible combination for classification and does not take into account the ability of class separability for each gene. In this paper, a novel SVM-RFE based on energy distance (ED) and called SVM-RFE-ED is proposed to overcome the limitation of standard SVM-RFE. The aims of our study are to achieve a high classification accuracy rate and improve the classification model. The experimentation is conducted on five widely used datasets. Experimental results indicate that the proposed approach SVM-RFE-ED provides good results and achieve a high classification accuracy rate using a small number of genes.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Cancer diagnosis]]></kwd>
<kwd lng="en"><![CDATA[support vector machine]]></kwd>
<kwd lng="en"><![CDATA[recursive feature elimination]]></kwd>
<kwd lng="en"><![CDATA[gene selection]]></kwd>
<kwd lng="en"><![CDATA[energy distance]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
</kwd-group>
</article-meta>
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