Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach

Authors:
Estevam R. Jr. Hruschka;Edimilson B. dos Santos;Sebastian D. C. de O. Galvao
Affiliations:
Universidade Federal de Sao Carlos, Brazil.;Universidade Federal de Sao Carlos, Brazil.;Universidade Federal de Sao Carlos, Brazil.
Venue:
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Year:
2007

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Abstract

This work proposes, implements and discusses a hybrid Bayes/Genetic collaboration (VOGACMarkovPC) designed to induce Conditional Independence Bayesian Classifiers from data. The main contribution is the use of MarkovPC algorithm in order to reduce the computational complexity of a Genetic Algorithm (GA) designed to explore the Variable Orderings (VOs) in order to optimize the induced classifiers. Experiments performed in a number of datasets revealed that VOGAC-MarkovPC required less than 25% of the time demanded by VOGAC-PC on average. In addition, when concerning the classification accuracy, VOGAC-MakovPC performed as well as VOGAC-PC did.