IMPROVE NA(I)VE BAYES CLASSIFICATION WITH RELATED ITEMS
英文摘要
Naive Bayes classifier is based on the hypothesis that parameters of the sample are mutually conditional independent.The practical application of this hypothesis is hard to established, so this paper proposes a new algorithm to improve Naive Bayes classifier through looking for properties that have the maximum influence on error classification with an effectively way, finding related items to extend the original data set, then adding weights to the related items.This paper shows the results by experiments, and related items make the classifier work better.
