WIT Press


A Feature Selection Bayesian Approach For A Clustering Genetic Algorithm

Price

Free (open access)

Paper DOI

10.2495/DATA030181

Volume

29

Pages

12

Published

2003

Size

634 kb

Author(s)

E. R. Hruschka Jr, E. R. Hruschka & N. F. F. Ebecken

Abstract

A feature selection Bayesian approach for a clustering genetic algorithm E. R. Hruschka ~ r ' , 3, E. R. ~ruschka~ & N. F. F. becke en' 'COPPE/ Universidade Federal do Rio de Janeiro, Brad 2~niversidade Cato'lica de Santos (UniSantos), Brasil 3 Universidade Tuiuti do Paranci, Brasil Abstract Feature selection is an important task in clustering problems. Some features help to find useful clusters whereas others may hinder the clustering process. In other words, some selected features can provide better clusters. Besides, the feature selection process also allows the reduction of the dataset dimensionality, improving the clustering method efficiency. This work describes a Bayesian feature selection approach for a Clustering Genetic Algorithm (CGA). The general method can be described by means of four steps: (i) apply the CGA to some selected objects (sample) of the complete dataset; (ii) consider that the obtained clusters form different classes, which can be modeled by Bayesian networks; (iii)

Keywords