Affiliations: [a] Federal University of S. Joao del-Rei – DCOMP/UFSJ, S. Joao del-Rei, MG, Brazil | [b] Federal University of S. Carlos – DC/UFSCar, S. Carlos, SP, Brazil | [c] FACCAMP – C. L. Paulista, SP, Brazil | [d] Federal University of Rio de Janeiro – COPPE/UFRJ, Rio de Janeiro, RJ, Brazil
Correspondence:
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Corresponding author: Edimilson B. dos Santos, Department of Computer Science, Federal University of São João del-Rei – UFSJ, Campus Tancredo de Almeida Neves – CTAN, Av. Visconde do Rio Preto, s/n° Colônia do Bengo, CEP 36301-360, São João del-Rei – MG, Brazil. Tel.: +55 32 3373 3985; E-mail: edimilson.santos@ufsj.edu.br
Abstract: Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs) and Bayesian Classifiers (BCs). Previous works in the literature suggest that it is worth pursuing the use of genetic/evolutionary algorithms for identifying a suitable VO, when learning a BN structure from data. This paper proposes a collaborative Evolutionary-Bayes algorithm named VOEA (Variable Ordering Evolutionary Algorithm) aimed at inducing BCs from data. The two VOEA versions presented in the paper refine a previously proposed algorithm named VOGA by employing only a single evolutionary operator (either crossover or mutation) as well as by using information about the class variable when defining the most suitable variable ordering for learning a BC. Experiments performed in a number of datasets revealed that the VOEA approach is promising and tends to generate suitable and representative BCs, particularly in its version VOEA_M, which only implements the mutation operator.
