Despite the simplicity and naive assumption of the
Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches,perhaps influenced by attribute weighting in other machine learning algorithms, use weighting to place more emphasis on highly predictive attributes than those that are less predictive. We argue that for naive Bayes attribute weighting should instead be used to relax the conditional independence assumption. RAW is Weka add-on that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions.
