<?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>S1405-55462019000200601</article-id>
<article-id pub-id-type="doi">10.13053/cys-23-2-3026</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estudio empírico del enfoque asociativo en el contexto de los problemas de clasificación]]></article-title>
<article-title xml:lang="en"><![CDATA[Empirical Study of the Associative Approach in the Context of Classification Problems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cleofas Sánchez]]></surname>
<given-names><![CDATA[Laura]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pineda Briseño]]></surname>
<given-names><![CDATA[Anabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valdovinos Rosas]]></surname>
<given-names><![CDATA[Rosa María]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Garreta]]></surname>
<given-names><![CDATA[José Salvador]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Jiménez]]></surname>
<given-names><![CDATA[Vicente]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Camacho Nieto]]></surname>
<given-names><![CDATA[Oscar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Meana]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nakano Miyatake]]></surname>
<given-names><![CDATA[Mariko]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Ingeniería Mecánica y Eléctrica ]]></institution>
<addr-line><![CDATA[México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Matamoros ]]></institution>
<addr-line><![CDATA[Matamoros Tamaulipas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma del Estado de México Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[Toluca ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Jaume I Instituto de Nuevas Tecnologías de la Imagen Departamento de Lenguajes y Sistemas de la Informática]]></institution>
<addr-line><![CDATA[Castellón de la Plana ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Autónoma de la Ciudad de Juárez Departamento de Ingeniería Eléctrica y Computación ]]></institution>
<addr-line><![CDATA[Ciudad de Juárez Chihuahua]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de Investigación en Computación ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>23</volume>
<numero>2</numero>
<fpage>601</fpage>
<lpage>617</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462019000200601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462019000200601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462019000200601&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Investigaciones realizadas por la comunidad científica han evidenciado que el rendimiento de los clasificadores, no solamente depende de la regla de aprendizaje, sino también de las complejidades inherentes en los conjuntos de datos. Algunos clasificadores se han utilizado habitualmente en el contexto de los problemas de clasificación (tres Redes neuronales, C4.5, SVM, entre otros). No obstante, el enfoque asociativo se ha explorado más en en el ámbito de recuperación, que en la tarea de clasificación, y su rendimiento se ha analizado escasamente cuando se presentan varias complejidades en los datos. La presente investigación analiza el rendimiento del enfoque asociativo (CHA, CHAT y Alfa Beta original) cuando se presentan tres problemas de clasificación (desequilibrio de las clases, solapamiento y patrones atípicos). Los resultados evidencian que el CHAT reconoce mejor la clase minoritaria en comparación con el resto de los clasificadores en el contexto del desequilibrio de las clases. Sin embargo, el modelo CHA ignora la clase minoritaria en la mayoría de los casos. Además, el modelo CHAT exhibe la necesidad de requerir de fronteras de decisión bien definidas cuando se aplica el método de Wilson, ya que su rendimiento se incrementa. También, se notó que cuando se enfatiza un equilibrio entre las tasas, el rendimiento de tres clasificadores incrementa (CHAT, RB y RFBR). El modelo Alfa beta original sigue mostrando un desempeño pobre cuando se realiza el pre-procesamiento en los datos. El rendimiento de los clasificadores incrementa significativamente al aplicarse el método SMOTE, situación que no se presenta sin un pre-procesamiento o submuestreo, en el contexto del desequilibrio de las clases.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Research carried out by the scientific community has shown that the performance of the classifiers depends not only on the learning rule, if not also on the complexities inherent in the data sets. Some traditional classifiers have been commonly used in the context of classification problems (three Neural Networks, C4.5, SVM, among others). However, the associative approach has been further explored in the recovery context, than in the classification task, and its performance almost has not been analyzed when several complexities in the data are presented. The present investigation analyzes the performance of the associative approach (CHA, CHAT and original Alpha Beta) when three classification problems occur (class imbalance, overlapping and atypical patterns). The results show that the CHAT algorithm recognizes the minority class better than the rest of the classifiers in the context of class imbalance. However, the CHA model ignores the minority class in most cases. In addition, the CHAT algorithm requires well-defined decisión boundaries when Wilson's method is applied, because of its performance increases. Also, it was noted that when a balance between the rates is emphasized, the performance of the three classifiers increase (RB, RFBR and CHAT). The original Alfa Beta model shows poor performance when pre-processing the data is done. The performance of the classifiers increases significantly when the SMOTE method is applied, which does not occur without a pre-processing or with a subsampling, in the context of the imbalance of the classes.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Recuperación]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[enfoque asociativo]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[C4.5]]></kwd>
<kwd lng="es"><![CDATA[SVM]]></kwd>
<kwd lng="es"><![CDATA[desequilibrio]]></kwd>
<kwd lng="es"><![CDATA[solapamiento]]></kwd>
<kwd lng="es"><![CDATA[patrones atípicos]]></kwd>
<kwd lng="es"><![CDATA[Wilson]]></kwd>
<kwd lng="es"><![CDATA[selectivo]]></kwd>
<kwd lng="es"><![CDATA[SMOTE]]></kwd>
<kwd lng="en"><![CDATA[Recovery]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[associative approach]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[C4.5]]></kwd>
<kwd lng="en"><![CDATA[SVM]]></kwd>
<kwd lng="en"><![CDATA[imbalance]]></kwd>
<kwd lng="en"><![CDATA[overlap]]></kwd>
<kwd lng="en"><![CDATA[atypical patterns]]></kwd>
<kwd lng="en"><![CDATA[Wilson]]></kwd>
<kwd lng="en"><![CDATA[selective]]></kwd>
<kwd lng="en"><![CDATA[SMOTE]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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