Feature selection based on quality of information

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• 作者：Jinghua Liu^a ; ^b ; ^{zzliujinghua@163.com}Author Vitae ; Yaojin Lin^b ; ^{yjlin@mnnu.edu.cn}Author Vitae ; Menglei Lin^b ; ^{menglei36@126.com}Author Vitae ; Shunxiang Wu^a ; ^{sxwu@xmu.edu.cn}Author Vitae ; Jia Zhang^b ; ^{zhangjia_gl@163.com}Author Vitae
• 关键词：Feature selection ; Information entropy ; Maximum-nearest-neighbor ; Quality of information
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：225
• 期：Complete
• 页码：11-22
• 全文大小：1259 K
• 卷排序：225

文摘

Feature selection as one of the key problems of data preprocessing is a hot research topic in pattern recognition, machine learning, and data mining. Evaluating the relevance between features based on information theory is a popular and effective method. However, very little research pays attention to the distinguishing ability of feature, i.e., the degree of a feature distinguishes a given sample with other samples. In this paper, we propose a new feature selection method based on the distinguishing ability of feature. First, we define the concept of maximum-nearest-neighbor, and use this concept to discriminate the nearest neighbors of samples. Then, we present a new measure method for evaluating the quality of feature. Finally, the proposed algorithm is tested on benchmark datasets. Experimental results show that the proposed algorithm can effectively select a discriminative feature subset, and performs as well as or better than other popular feature selection algorithms.