Conditional independence based learning of bayesian classifiers guided by a variable ordering genetic search
Edimilson Batista dos Santos, Estevam Rafael Hruschka Junior, Maria do Carmo Nicoletti · 2007
This work proposes a genetic strategy for learning a Bayesian classifier using an algorithm based on conditional independence and the information given by a variable ordering. The strategy has been implemented as the system VOGAC-PC. The paper presents and analyses the results of experiments in various domains using VOGAC-PC as well as a previous system, named VOGA-K2, based on algorithm K2.