Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures
In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Parallelization was implemented on multi-core multi-threaded architectures, using the Flynn Single Instruction Multiple Data taxonomy. This technique was used f...
- Autores:
-
Uribe-Hurtado, Ana Lorena
Villegas-Jaramillo, Eduardo-Jose
Orozco-Alzate, Mauricio
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/13192
- Acceso en línea:
- http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867
http://hdl.handle.net/10784/13192
- Palabra clave:
- Multi-core computing
Classification algorithms
Leave-one-out test
Computación con múltiples núcleos
Algoritmos de clasificación
Prueba leave-one-ou
- Rights
- License
- Attribution 4.0 International (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| dc.title.spa.fl_str_mv |
Evaluación leave-one-out de los clasificadores de la línea de características más cercana y del segmento de línea rectificado más cercano usando arquitecturas multi-núcleo |
| title |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| spellingShingle |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures Multi-core computing Classification algorithms Leave-one-out test Computación con múltiples núcleos Algoritmos de clasificación Prueba leave-one-ou |
| title_short |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| title_full |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| title_fullStr |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| title_full_unstemmed |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| title_sort |
Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core Architectures |
| dc.creator.fl_str_mv |
Uribe-Hurtado, Ana Lorena Villegas-Jaramillo, Eduardo-Jose Orozco-Alzate, Mauricio |
| dc.contributor.author.none.fl_str_mv |
Uribe-Hurtado, Ana Lorena Villegas-Jaramillo, Eduardo-Jose Orozco-Alzate, Mauricio |
| dc.contributor.affiliation.spa.fl_str_mv |
Universidad Nacional de Colombia sede Manizales Universidad Nacional de Colombia |
| dc.subject.keyword.eng.fl_str_mv |
Multi-core computing Classification algorithms Leave-one-out test |
| topic |
Multi-core computing Classification algorithms Leave-one-out test Computación con múltiples núcleos Algoritmos de clasificación Prueba leave-one-ou |
| dc.subject.keyword.spa.fl_str_mv |
Computación con múltiples núcleos Algoritmos de clasificación Prueba leave-one-ou |
| description |
In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Parallelization was implemented on multi-core multi-threaded architectures, using 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(n4). 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. |
| publishDate |
2018 |
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2018-11-16T16:28:59Z |
| dc.date.issued.none.fl_str_mv |
2018-06-14 |
| dc.date.accessioned.none.fl_str_mv |
2018-11-16T16:28:59Z |
| dc.date.none.fl_str_mv |
2018-06-14 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion article publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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Artículo |
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publishedVersion |
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2256-4314 1794-9165 |
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http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867 http://hdl.handle.net/10784/13192 |
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10.17230/ingciencia.13.27.4 |
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2256-4314 1794-9165 10.17230/ingciencia.13.27.4 |
| url |
http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867 http://hdl.handle.net/10784/13192 |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
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http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867 |
| dc.rights.eng.fl_str_mv |
Attribution 4.0 International (CC BY 4.0) |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by/4.0 |
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Acceso abierto |
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Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0 Acceso abierto http://purl.org/coar/access_right/c_abf2 |
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Ingeniería y Ciencia; Vol 14 No 27 (2018); 75-99 |
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Ingeniería y Ciencia; Vol 14 No 27 (2018); 75-99 |
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2018-06-142018-11-16T16:28:59Z2018-06-142018-11-16T16:28:59Z2256-43141794-9165http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867http://hdl.handle.net/10784/1319210.17230/ingciencia.13.27.4In this paper we present the parallelization of the leave-one-out test: a reproducible test that is, in general, computationally expensive. Parallelization was implemented on multi-core multi-threaded architectures, using 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(n4). 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.Presentamos en este artículo la paralelización de la prueba leave-one-out, la cual es una prueba repetible pero que, en general, resulta costosa computacionalmente. La paralelización se implementó sobre arquitecturas multinúcleo con múltiples hilos, usando la taxonomía Flynn Single Instruction Multiple Data. Esta técnica se empleó para las etapas de preproceso y proceso 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 algoritmo más costoso —RNFLS— cuya complejidad es O(n4). 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.application/pdfengUniversidad EAFIThttp://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/4867Copyright (c) 2018 Ana Lorena Uribe-Hurtado, Eduardo-Jose Villegas-Jaramillo, Mauricio Orozco-AlzateAttribution 4.0 International (CC BY 4.0)http://creativecommons.org/licenses/by/4.0Acceso abiertohttp://purl.org/coar/access_right/c_abf2instname:Universidad EAFITreponame:Repositorio Institucional Universidad EAFITIngeniería y Ciencia; Vol 14 No 27 (2018); 75-99Ingeniería y Ciencia; Vol 14 No 27 (2018); 75-99Leave-one-out Evaluation of the Nearest Feature Line and the Rectified Nearest Feature Line Segment Classifiers Using Multi-core ArchitecturesEvaluación leave-one-out de los clasificadores de la línea de características más cercana y del segmento de línea rectificado más cercano usando arquitecturas multi-núcleoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionarticlepublishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Multi-core computingClassification algorithmsLeave-one-out testComputación con múltiples núcleosAlgoritmos de clasificaciónPrueba leave-one-ouUribe-Hurtado, Ana LorenaVillegas-Jaramillo, Eduardo-JoseOrozco-Alzate, MauricioUniversidad Nacional de Colombia sede ManizalesUniversidad Nacional de ColombiaIngeniería y Ciencia14277599ing.ciencTHUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/d99a7839-a7b9-4bd9-b48c-5b21ae224f66/downloadda9b21a5c7e00c7f1127cef8e97035e0MD51ORIGINALdocument (4).pdfdocument (4).pdfTexto completo PDFapplication/pdf826697https://repository.eafit.edu.co/bitstreams/e981cd01-488c-464e-b85b-4aa5679caaf9/download2166f85e510370a730ca215aba4b28c0MD52articulo.htmlarticulo.htmlTexto completo HTMLtext/html374https://repository.eafit.edu.co/bitstreams/e4dbcc43-0eef-430b-840e-772737ecdea0/downloade05d441677441e6293db0f1a57caf5e4MD5310784/13192oai:repository.eafit.edu.co:10784/131922020-03-01 12:48:13.032http://creativecommons.org/licenses/by/4.0Copyright (c) 2018 Ana Lorena Uribe-Hurtado, Eduardo-Jose Villegas-Jaramillo, Mauricio Orozco-Alzateopen.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co |