A Feature Selection Bayesian Approach For A Clustering
Genetic Algorithm
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
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