Abstract

Naive Bayesian learning has been popular in data mining applications. However, the performance of naive Bayesian learning is sometimes poor due to the unrealistic assumption that all features are equally important and independent given the class value. Therefore, it is widely known that the performance of naive Bayesian learning can be improved by mitigating this assumption, and many enhancements to the basic naive Bayesian learning have been proposed to resolve this problem including feature selection and feature weighting. In this paper, we propose a new method for calculating the weights of features in naive Bayesian learning using Kullback-Leibler measure. Empirical results are presented comparing this new feature weighting method with some other methods for a number of datasets.