Affiliations: [a] School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China | [b] Shaanxi Key Laboratory of Network Computing and Security Technology, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Hongfang Zhou, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, China. E-mail: zhouhf@xaut.edu.cn.
Abstract: Feature interaction is crucial in the process of feature selection. In this paper, a grouping feature selection method based on feature interaction (GFS-NPIS) is proposed. Firstly, a new evaluation function measuring feature interaction is proposed. Secondly, a grouping strategy based on approximate Markov blanket is used to remove strong redundant features. Lastly, a new feature selection method called as GFS-NPIS is given. In order to verify the effectiveness of our method, we compare GFS-NPIS with other eight representative ones on three classifiers (SVM, KNN and CART). The experimental results on fifteen public data sets show that GFS-NPIS outperforms others in terms of classification accuracy and Macro-F1.
