Learning weighted naive Bayes with accurate ranking

Naive Bayes is one of most effective classification algorithms. In many applications, however, a ranking of examples are more desirable than just classification. How to extend naive Bayes to improve its ranking performance is an interesting and useful question in practice. Weighted naive Bayes is an extension of naive Bayes, in which attributes have different weights. This paper investigates how to learn a weighted naive Bayes with accurate ranking from data, or more precisely, how to learn the weights of a weighted naive Bayes to produce accurate ranking. We explore various methods: the gain ratio method, the hill climbing method, and the Markov chain Monte Carlo method, the hill climbing method combined with the gain ratio method, and the Markov chain Monte Carlo method combined with the gain ratio method. Our experiments show that a weighted naive Bayes trained to produce accurate ranking outperforms naive Bayes.