TY - GEN

T1 - Learning weighted naive bayes with accurate ranking

AU - Zhang, Harry

AU - Sheng, Shengli

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=19544366243&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2004.10030

DO - 10.1109/ICDM.2004.10030

M3 - Conference contribution

AN - SCOPUS:19544366243

SN - 0769521428

SN - 9780769521428

T3 - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

SP - 567

EP - 570

BT - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

A2 - Rastogi, R.

A2 - Morik, K.

A2 - Bramer, M.

A2 - Wu, X.

Y2 - 1 November 2004 through 4 November 2004

ER -