<?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>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282019000300079</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Obtención de predicados difusos con un enfoque multiobjetivo: comparación de dos variantes]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lapeira]]></surname>
<given-names><![CDATA[Orenia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ceruto]]></surname>
<given-names><![CDATA[Taymi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rosete]]></surname>
<given-names><![CDATA[Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnologica de la Habana &#8220;José A Echeverría&#8221; (CUJAE)  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>3</numero>
<fpage>79</fpage>
<lpage>91</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282019000300079&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282019000300079&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282019000300079&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN FuzzyPred es un algoritmo de aprendizaje no supervisado de minería de datos, que permite extraer predicados difusos en forma normal a partir de los datos. Este método se utiliza para resolver una tarea descriptiva donde no se conoce a ciencia cierta qué tipo de relaciones se van a encontrar. Se trata de encontrar patrones que describan los datos y sus relaciones. Debido al gran conjunto de soluciones o espacio de búsqueda que puede tener, fue modelado como un problema de optimización, donde se aplican las metaheurísticas como vía de solución para encontrar buenas soluciones. FuzzyPred brinda como resultado un conjunto de predicados, evaluados en cada una de las medidas de calidad, aunque solo optimiza una de estas medidas. Este trabajo analiza vías para enfocar FuzzyPred como un problema de optimización multiobjetivo. Por esto, se introducen en el problema dos de las técnicas principales de optimización multiobjetivo: la técnica basada en Pareto (o multiobjetivo puro) y la de los factores ponderados. Se realiza un estudio experimental comparativo entre ambas técnicas en este problema para conocer la eficacia de estas técnicas. Los resultados en varias bases de datos internacionales demuestran que se obtienen mejores resultados con la técnica multiobjetivo puro.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT FuzzyPred is an unsupervised-learning data-mining algorithm, which allows extracting fuzzy predicates in normal forms from the data. This method is used to solve a descriptive task where it is not known the kind of relationships that are to be found. The goal is to find patterns that describe the data and their relationships. Due to the large set of solutions or search space that may have, it was modeled as an optimization problem, where metaheuristics are applied to find good solutions. FuzzyPred provides as a result a set of predicates, evaluated in each of the quality measures, in spite of the fact that only one of the measures is optimized (truth value). This paper introduces a multiobjective approach for FuzzyPred by using two of the most known multi-objective optimization techniques: Pareto technique (or pure multi-objective) and weighted factors. An experimental study is presented in order to compare the efficacy of both techniques in this problem. The results in several international databases show that better results are obtained by the pure multi-objective technique.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Minería de Datos]]></kwd>
<kwd lng="es"><![CDATA[Predicados Difusos]]></kwd>
<kwd lng="es"><![CDATA[Técnicas de Optimización Multiobjetivo]]></kwd>
<kwd lng="en"><![CDATA[Data Mining]]></kwd>
<kwd lng="en"><![CDATA[Fuzzy Predicates]]></kwd>
<kwd lng="en"><![CDATA[Multiobjective Optimization Techniques]]></kwd>
</kwd-group>
</article-meta>
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