Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach
Estevam Rafael Hruschka Junior, Edimilson Batista dos Santos, Sebastian D. C. de O. Galvão · 2007
This work proposes, implements and discusses a hybrid Bayes/genetic collaboration (VOGAC-MarkovPC) 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.