A Discriminative Learning Method of TAN Classifier

摘要

TAN (Tree-augmented Naive Bayes) classifier makes a compromise between the model complexity and classification rate, the study of which has now become a hot research issue. In this paper, we propose a discriminative method that is based on KL (Kullback-Leibler) divergence to learn TAN classifier. First, we use EAR (explaining away residual) method to learn the structure of TAN, and then optimize TAN parameters by an objective function based on KL divergence. The results of the experiments on benchmark datasets show that our approach produces better classification rate.