Neighborhood effective information ratio for hybrid feature subset evaluation and selection

Authors:
Wenzhi Zhu;Gangquan Si;Yanbin Zhang;Jingcheng Wang
Affiliations:
State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
Venue:
Neurocomputing
Year:
2013

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Abstract

Feature selection has been widely discussed as an important preprocessing step in machine learning and data mining. Evaluation criterion designing arises as a main aspect for constructing feature selection algorithms. In this paper, a new feature evaluation criterion, called the neighborhood effective information ratio (NEIR), is proposed to compute discernibility capability of categorical and numerical features. Based on the evaluation criterion, a general definition of significance of hybrid features is presented. Then a greedy selection algorithm for hybrid feature subsets based on the proposed evaluation criterion is constructed for data classification. We compare the proposed algorithm with other feature selection algorithms. Both theoretical and experimental analysis verifies the effectiveness and the efficiency of the proposed algorithm.