<?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>1794-9165</journal-id>
<journal-title><![CDATA[Ingeniería y Ciencia]]></journal-title>
<abbrev-journal-title><![CDATA[ing.cienc.]]></abbrev-journal-title>
<issn>1794-9165</issn>
<publisher>
<publisher-name><![CDATA[Escuela de Ciencias y Humanidades y Escuela de Ingeniería de la Universidad EAFIT]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1794-91652018000100075</article-id>
<article-id pub-id-type="doi">10.17230/ingciencia.14.27.4</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures]]></article-title>
<article-title xml:lang="es"><![CDATA[Evaluación leave-one-out de los clasificadores de la línea de características más cercana y del segmento de línea rectiftcado más cercano usando arquitecturas multi-núcleo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Uribe-Hurtado]]></surname>
<given-names><![CDATA[Ana-Lorena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villegas-Jaramillo]]></surname>
<given-names><![CDATA[Eduardo-José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orozco-Alzate]]></surname>
<given-names><![CDATA[Mauricio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</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>14</volume>
<numero>27</numero>
<fpage>75</fpage>
<lpage>99</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1794-91652018000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1794-91652018000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1794-91652018000100075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Paralelization was implemented on multi-core multi-threaded architectures, us- ing the Flynn Single Instruction Multiple Data taxonomy. This technique was used for the preprocessing and processing stages of two classification algorithms that are oriented to enrich the representation in small sample cases: the nearest feature line (NFL) algorithm and the rectified nearest feature line segment (RNFLS) algorithm. Results show an acceleration of up to 18.17 times with the smallest dataset and 29.91 times with the largest one, using the most costly algorithm (RNFLS) whose complexity is O(n 4). The paper also shows the pseudo-codes of the serial and parallel algorithms using, in the latter case, a notation that describes the way the parallelization was carried out as a function of the threads.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Presentamos en este artículo la paralelización de la prueba leave-one-out, la cual es una prueba repetible pero que, en general, resulta costosa compu- tacionalmente. La paralelización se implementó sobre arquitecturas multi- núcleo con múltiples hilos, usando la taxonomía Flynn Single Instruction Multiple Data. Esta técnica se empleó para las etapas de preproceso y pro- ceso de dos algoritmos de clasificación que están orientados a enriquecer la representación en casos de muestra pequeña: el algoritmo de la línea de características más cercana (NFL) y el algoritmo del segmento de línea rectificado más cercano (RNFLS). Los resultados obtenidos muestran una aceleración de hasta 18.17 veces con el conjunto de datos mas pequeño y de 29.91 veces con el conjunto de datos más grande, empleando el al- goritmo más costoso -RNFLS- cuya complejidad es O(n 4). El artículo muestra también los pseudocódigos de los algoritmos seriales y paralelos empleando, en este último caso, una notación que describe la manera como se realizó la paralelización en función de los hilos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Multi-core computing]]></kwd>
<kwd lng="en"><![CDATA[classification algorithms]]></kwd>
<kwd lng="en"><![CDATA[leave-one-out test]]></kwd>
<kwd lng="es"><![CDATA[Computación con múltiples núcleos]]></kwd>
<kwd lng="es"><![CDATA[algoritmos de clasificación]]></kwd>
<kwd lng="es"><![CDATA[prueba leave-one-out]]></kwd>
</kwd-group>
</article-meta>
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