<?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>2304-0106</journal-id>
<journal-title><![CDATA[Anales de la Academia de Ciencias de Cuba]]></journal-title>
<abbrev-journal-title><![CDATA[Anales de la ACC]]></abbrev-journal-title>
<issn>2304-0106</issn>
<publisher>
<publisher-name><![CDATA[Academia de Ciencias de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2304-01062023000300008</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Desarrollo de técnicas para el preprocesamiento y la predicción de problemas de clasificación multietiqueta]]></article-title>
<article-title xml:lang="en"><![CDATA[Development of techniques for pre-processing and prediction of multi-label classification problems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bello García]]></surname>
<given-names><![CDATA[Marilyn]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bello Pérez]]></surname>
<given-names><![CDATA[Rafael E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nápoles]]></surname>
<given-names><![CDATA[Gonzalo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vanhoof]]></surname>
<given-names><![CDATA[Koen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Lorenzo]]></surname>
<given-names><![CDATA[María M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguilera Calzadilla]]></surname>
<given-names><![CDATA[Yaumara]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central Marta Abreu de Las Villas Centro de Investigaciones de la Informática ]]></institution>
<addr-line><![CDATA[ Santa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Tilburg Departmento de Ciencia Cognitiva e Inteligencia Artificial ]]></institution>
<addr-line><![CDATA[ Tilburg]]></addr-line>
<country>Países Bajos</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Hasselt Facultad de Negocios y Economía ]]></institution>
<addr-line><![CDATA[ Hasselt]]></addr-line>
<country>Bélgica</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Hospital Comandante Manuel Fajardo Rivero  ]]></institution>
<addr-line><![CDATA[ Santa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>13</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2304-01062023000300008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2304-01062023000300008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2304-01062023000300008&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  La clasificación multietiqueta es una variante de la clasificación tradicional de etiqueta única, en la que un objeto ya no se clasifica exclusivamente por una etiqueta. En su lugar, este aprendizaje pretende asignar a un objeto una o más clases de etiquetas de un conjunto predefinido de clases. Dado que el aprendizaje multietiqueta se encuentra todavía en una fase temprana de desarrollo, en comparación con otras técnicas de clasificación, algunas técnicas actualmente disponibles para otros tipos de aprendizaje no se han desarrollado para este caso específico.  Métodos:  Tras un estudio de la literatura existente, los siguientes son algunos de los retos de investigación dentro de esta temática: medidas de calidad de los datos, métodos de reducción sobre conjuntos de datos multietiqueta, métodos de detección de valores atípicos, capas de agrupación para datos multietiqueta sin una organización topológica, métodos para tratar problemas de clasificación multietiqueta con características dispersas y técnicas de inteligencia artificial explicable para clasificadores neuronales multietiqueta.  Resultados:  Se proponen: a) Medidas de calidad de los datos multietiqueta (3); b) Métodos para reducir conjuntos de datos multietiqueta (6); c) Método que mide el grado de anomalía de un objeto en conjuntos de datos multietiqueta (1); d) Arquitectura neuronal profunda que utiliza capas de agrupación basadas en la asociación bidireccional (1); e) Sistema neuronal para resolver problemas de clasificación multietiqueta descritos por datos tabulares que pueden implicar características dispersas (1) y f) Adaptación al escenario multietiqueta de una técnica clásica de interpretabilidad post-hoc en redes neuronales (1). Conclusiones, los métodos propuestos le proporcionan a la comunidad científica novedosas técnicas de clasificación multietiqueta, haciendo posible un proceso de descubrimiento de conocimiento más eficiente y eficaz sobre datos multietiqueta.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Multi-label classification is a variant of traditional single-label classification, where an object is no longer classified by exclusively one label. Instead, this learning aims to assign one or more classes from a predefined set of classes to an object. Since multi-label learning is still in an early development stage compared to other classification techniques, some techniques currently available for other learning types have not been developed for this specific learning case.  Methods:  After a survey of the existing literature, the following are some research challenges within this topic: data quality measures, reduction methods on multi-label datasets, outlier detection methods, pooling layers for multi-label data without a topological organization, methods to deal with multi-label classification problems with sparse features, and Explainable Artificial Intelligence techniques for multi-label neural classifiers.  Results:  We propose: a) three measures of multi-label data quality, b) six methods for reducing multi-label datasets, c) a method that measures an object's anomaly degree in a multi-label dataset, d) a deep neural architecture using bidirectional association-based pooling layers, e) a neural system to solve multi-label classification problems described by tabular data that might involve sparse features, and f) an adaptation to the multi-label scenario of a classical post-hoc interpretability technique on neural networks. Conclusions, the proposed methods provide the scientific community with novel multi-label classification techniques, making possible a more efficient and effective knowledge discovery process on multi-label data.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[clasificación multietiqueta]]></kwd>
<kwd lng="es"><![CDATA[caracterización de los datos]]></kwd>
<kwd lng="es"><![CDATA[preprocesamiento de los datos]]></kwd>
<kwd lng="es"><![CDATA[proceso de aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial explicable]]></kwd>
<kwd lng="en"><![CDATA[multi-label classification]]></kwd>
<kwd lng="en"><![CDATA[data characterization]]></kwd>
<kwd lng="en"><![CDATA[data pre-processing]]></kwd>
<kwd lng="en"><![CDATA[learning process]]></kwd>
<kwd lng="en"><![CDATA[explainable artificial intelligence.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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