降低权重冗余的分类算法CFS-CFW研究OA

Naive Bayes has a strong independence assumption,and feature weighting is the method to solve this problem.The CFW algorithm is a simple and effective weighting algorithm,but its weight calculation formula incorporates feature redundancy,which affects the weight assigned to each feature and reduces classification accuracy.In response to the weight redundancy problem in the CFW algorithm,this paper proposes the CFS-CFW algorithm.This algorithm uses the feature selection algorithm CFS to effectively reduce weight redundancy,allowing each feature to be assigned more appropriate weights.The experimental results on 13 UCI datasets show that the algorithm has higher classification accuracy.The accuracy of this algorithm is also higher on the spam classification dataset of UCI's spam database.