计算机工程与应用2017，Vol.53Issue(22)：105-110,115,7.DOI:10.3778/j.issn.1002-8331.1605-0293
基于归一化模糊联合互信息最大的特征选择
Feature selection using normalized fuzzy joint mutual information maximum
摘要
Abstract
Feature selection is the method that selects feature subset that has strong relevancy between features and classi-fication and smallest redundancy among features from feature set. This can improve the classifier's computational effi-ciency, and enhance the classifier's generalization, and therefore increase classification accuracy. However, the relevance and redundancy evaluation criteria based on mutual information has the following problems in the practical applications:(1)It is difficult to calculate the probability of a variable and the feature's information entropy;(2)The approach based on mutual information tends to choose features which have more values;(3)The method measuring redundancy between candidate features and selected feature subset based on cumulative addition with higher dimension data sets always is inval-id. To solve the above problems, the feature evaluation criteria based on Normalized Fuzzy Joint Mutual Information Max-imum(NFJMIM)is proposed in this paper. Firstly, the entropy, conditional entropy, joint entropy of a variable are calcu-lated based on fuzzy equivalence relation. Secondly, the feature's importance is evaluated base on NFJMIM. Finally, using the established criteria, forward greedy search approach is used for searching feature subset. Several experiments using UCI machine learning repository prove that the proposed algorithm can effectively select effective feature subset, and can significantly improve the classification accuracy.关键词
模糊等价关系/联合互信息/最大最小准则/特征选择Key words
fuzzy equivalence relations/joint mutual information/the maximum and minimum criteria/feature selection分类
信息技术与安全科学引用本文复制引用
董泽民,石强..基于归一化模糊联合互信息最大的特征选择[J].计算机工程与应用,2017,53(22):105-110,115,7.基金项目
国家自然科学基金(No.61472289) （No.61472289）
中央高校基本科研业务费专项资金(No.2015IVA067) （No.2015IVA067）
河南省教育厅自然科学基金(No.15A110011). （No.15A110011）
