Feature Selection for Ensembles of Simple Bayesian Classifiers

Abstract

A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. However, the simple Bayesian classifier has much broader applicability than previously thought. Besides its high classification accuracy, it also has advantages in terms of simplicity, learning speed, classification speed, storage space, and incrementality. One way to generate an ensemble of simple Bayesian classifiers is to use different feature subsets as in the random subspace method. In this paper we present a technique for building ensembles of simple Bayesian classifiers in random subspaces. We consider also a hill-climbing-based refinement cycle, which improves accuracy and diversity of the base classifiers. We conduct a number of experiments on a collection of real-world and synthetic data sets. In many cases the ensembles of simple Bayesian classifiers have significantly higher accuracy than the single “global” simple Bayesian classifier. We consider several methods for integration of simple Bayesian classifiers. The dynamic integration better utilizes ensemble diversity than the static integration.