An information-theoretic filter approach for value weighted classification learning in naive Bayes

Assigning weights in features has been an important topic in some classification learning algorithms. In this paper, we propose a new paradigm of assigning weights in classification learning, called value weighting method. While the current weighting methods assign a weight to each feature, we assign a different weight to the values of each feature. The performance of naive Bayes learning with value weighting method is compared with that of some other traditional methods for a number of datasets. The experimental results show that the value weighting method could improve the performance of naive Bayes significantly.