Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Systematic Comparison of Supervised Classifiers

Full text
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]
Total Authors: 7
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PLoS One; v. 9, n. 4 APR 24 2014.
Web of Science Citations: 67

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)

FAPESP's process: 13/06717-4 - Modeling human knowledge and behavior with complex networks
Grantee:Diego Raphael Amancio
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 10/00927-9 - Using complex networks to classify texts
Grantee:Diego Raphael Amancio
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 11/22639-8 - Unveiling the relationship between structure and dynamics on modular networks
Grantee:Cesar Henrique Comin
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 13/14984-2 - Texture fractal descriptors applied in the leaf identification and anatomical plasticity
Grantee:Dalcimar Casanova
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 10/19440-2 - Characterization, analysis, simulation and classification of complex networks
Grantee:Francisco Aparecido Rodrigues
Support Opportunities: Regular Research Grants
FAPESP's process: 14/04930-5 - A systematic comparison of supervised classifiers
Grantee:Odemir Martinez Bruno
Support Opportunities: Regular Research Grants - Publications - Scientific article
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants