Weighted Naive Bayes Classification Algorithm Based on Rough Set
基于Rough Set的加权朴素贝叶斯分类算法

Naive Bayes algorithm is an effective simple classification algorithm.Since its conditional independence assumption is not always true in real life,its classification performance is affected to some extent.Weighted naive Bayes(simply WNB)is an extension of it.Based on the attributes' importance degree theory of rough set,a new weighted naive Bayes method is proposed.Methods for determining the weights of attributes in the algebra view,informational view and both of them are developed respectively.Simulation results on a variety of UCI data sets illustrate the efficiency of this method.