A Systematic Comparison of Supervised Classifiers

Autor(es)

Amancio, Diego Raphael ^[1] ; Comin, Cesar Henrique ^[2] ; Casanova, Dalcimar ^[2] ; Travieso, Gonzalo ^[2] ; Bruno, Odemir Martinez ^[2] ; Rodrigues, Francisco Aparecido ^[1] ; Costa, Luciano da Fontoura ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 9, n. 4 APR 24 2014.
• Citações Web of Science: 67

Resumo

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. (AU)

• Processo FAPESP: 13/06717-4 - Modelagem do conhecimento e comportamento com redes complexas
• Beneficiário: Diego Raphael Amancio
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 10/00927-9 - Classificação de textos com redes complexas
• Beneficiário: Diego Raphael Amancio
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 11/22639-8 - Estudo da relação estrutura-dinâmica em redes modulares
• Beneficiário: Cesar Henrique Comin
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 13/14984-2 - Descritores fractais de texturas aplicados na identificação foliar e plasticidade anatômica
• Beneficiário: Dalcimar Casanova
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 10/19440-2 - Caracterização, análise, simulação e classificação de redes complexas
• Beneficiário: Francisco Aparecido Rodrigues
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/04930-5 - A systematic comparison of supervised classifiers
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Publicações científicas - Artigo
• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático