福建电脑2024，Vol.40Issue(1)：9-15,7.DOI:10.16707/j.cnki.fjpc.2024.01.002
降低权重冗余的分类算法CFS-CFW研究
A Classification Algorithm CFS-CFW for Reducing Weight Redundancy
摘要
Abstract
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.关键词
特征加权/特征选择/朴素贝叶斯Key words
Feature Weighting/Feature Selection/Naive Bayes分类
信息技术与安全科学引用本文复制引用
黄丽媛,何振峰..降低权重冗余的分类算法CFS-CFW研究[J].福建电脑,2024,40(1):9-15,7.基金项目
本文得到福建省自然科学基金(No.2022J01574)资助. （No.2022J01574）
