A systematic comparison of supervised classifiers.

Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification i...

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Autores principales:	Diego Raphael Amancio, Cesar Henrique Comin, Dalcimar Casanova, Gonzalo Travieso, Odemir Martinez Bruno, Francisco Aparecido Rodrigues, Luciano da Fontoura Costa
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c90c70b459543248ed6e41d5f6df4dd
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spelling	oai:doaj.org-article:0c90c70b459543248ed6e41d5f6df4dd2021-11-18T08:21:35ZA systematic comparison of supervised classifiers.1932-620310.1371/journal.pone.0094137https://doaj.org/article/0c90c70b459543248ed6e41d5f6df4dd2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24763312/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.Diego Raphael AmancioCesar Henrique CominDalcimar CasanovaGonzalo TraviesoOdemir Martinez BrunoFrancisco Aparecido RodriguesLuciano da Fontoura CostaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e94137 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diego Raphael Amancio Cesar Henrique Comin Dalcimar Casanova Gonzalo Travieso Odemir Martinez Bruno Francisco Aparecido Rodrigues Luciano da Fontoura Costa A systematic comparison of supervised classifiers.
description	Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.
format	article
author	Diego Raphael Amancio Cesar Henrique Comin Dalcimar Casanova Gonzalo Travieso Odemir Martinez Bruno Francisco Aparecido Rodrigues Luciano da Fontoura Costa
author_facet	Diego Raphael Amancio Cesar Henrique Comin Dalcimar Casanova Gonzalo Travieso Odemir Martinez Bruno Francisco Aparecido Rodrigues Luciano da Fontoura Costa
author_sort	Diego Raphael Amancio
title	A systematic comparison of supervised classifiers.
title_short	A systematic comparison of supervised classifiers.
title_full	A systematic comparison of supervised classifiers.
title_fullStr	A systematic comparison of supervised classifiers.
title_full_unstemmed	A systematic comparison of supervised classifiers.
title_sort	systematic comparison of supervised classifiers.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0c90c70b459543248ed6e41d5f6df4dd
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A systematic comparison of supervised classifiers.

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