<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992019000100031</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Classifier ensemble algorithm for learning from non-stationary data stream]]></article-title>
<article-title xml:lang="es"><![CDATA[Ensamble de clasificadores para el aprendizaje a partir de flujos de datos no estacionarios.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Verdecia Cabrera]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Frías Blanco]]></surname>
<given-names><![CDATA[Isvani]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Diaz]]></surname>
<given-names><![CDATA[Agustín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Zarabia]]></surname>
<given-names><![CDATA[Yanet]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Diez]]></surname>
<given-names><![CDATA[Héctor Raúl]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Granma Departamento de Informática. ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,LexisNexis Risk Solutions  ]]></institution>
<addr-line><![CDATA[ Sao Paulo]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,CCT-UDESC Santa Catarina State University  ]]></institution>
<addr-line><![CDATA[Joinville Santa Catarina]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Central Marta Abreu de las Villas Centro de investigaciones de la informática. ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad de las Ciencias Informáticas  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<volume>13</volume>
<numero>1</numero>
<fpage>31</fpage>
<lpage>44</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992019000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992019000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992019000100031&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Nowadays, many sources generate unbounded data streams at high incoming rates. It is impossible to store these large volumes of data and it is necessary to process them in real time. Because these data are acquired over time and the dynamism of many real world situations, the target function to be learned can change over time, a problem commonly called concept drift. This paper presents a new ensemble algorithm called Classifier Ensemble Algorithm (CEA), able for learning from data streams with concept drift. CEA manipulates these changes using a change detector in each base classifier. When the detector estimates a change, the classifier in which the change was estimated is replaced by a new one. CEA combines the simplicity of the bagging algorithm to train base classifiers and Exponentially Weighted Moving Average (EWMA) control charts to estimate the weights of each base classifier. The proposed algorithm is compared empirically with several bagging family ensemble algorithms able to learn from concept-drifting data. The experiments show promising results from the proposed algorithm (regarding accuracy), handling different types of concept drifts.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN En la actualidad, muchas fuentes generan flujos de datos ilimitados a altas tasas de entrada. Es imposible almacenar estos grandes volúmenes de datos por lo que es necesario procesarlos en tiempo real. Debido a que estos datos se adquieren a lo largo del tiempo y la dinámica de muchas situaciones reales, la función objetivo que se debe aprender puede cambiar con el tiempo, un problema que comúnmente conocido como cambio de concepto. En este artículo se presenta un nuevo algoritmo de ensamble denominado Algoritmo de Ensamble de Clasificadores (CEA), capaz de aprender de flujos de datos con cambios de concepto. CEA manipula estos cambios utilizando un detector de cambios en cada clasificador base. Cuando el detector estima un cambio, el clasificador en el que se estimó el cambio se reemplaza por uno nuevo. CEA combina la simplicidad del algoritmo de bagging para entrenar clasificadores base y el esadístico EWMA para estimar los pesos de cada clasificador base. El algoritmo propuesto se compara empíricamente con varios algoritmos de ensamble basados en bagging capaces de aprender de flujos de datos con cambios de concepto. Los experimentos muestran que el algoritmo propuesto muestra resultados prometedores (con respecto a la precisión), manipulando diferentes tipos de cambios de concepto.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[classifier ensemble]]></kwd>
<kwd lng="en"><![CDATA[concept drift]]></kwd>
<kwd lng="en"><![CDATA[data stream]]></kwd>
<kwd lng="es"><![CDATA[cambio de concepto]]></kwd>
<kwd lng="es"><![CDATA[ensamble de clasificadores]]></kwd>
<kwd lng="es"><![CDATA[flujos de datos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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