A Selective Approach for an Ensemble of Simple Bayesian Classifiers

To avoid the influence of feature reduction from data pre-processing on the performance of classification, a selective approach was proposed for an ensemble of simple Bayesian classifiers(ESBC), making use of random feature selection to generate several feature subsets from the whole training set, and obtained different simple Bayesian classifiers(SBCs) with the feature subsets, and then optimized the ESBC using genetic algorithms (GA), wherein the fitness function of GA involved the accuracy and diversity of SBCs. In the experiments, this approach was compared with existing methods in their performance through some standard data sets from the UCI machine learning repository, and was applied to the threat-degree estimation in C 3I.