A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm
Expert Systems with Applications: An International Journal
Data classification using rough sets and naïve Bayes
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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WAIM'11 Proceedings of the 12th international conference on Web-age information management
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Empirical Software Engineering
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
Learning attribute weighted AODE for ROC area ranking
International Journal of Information and Communication Technology
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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.