Unsupervised feature selection based on Markov blanket and particle swarm optimization

作     者：Yintong Wang Jiandong Wang Hao Liao Haiyan Chen 

作者机构：College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University College of Computer Science and Software Engineering Shenzhen University 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2017年第28卷第1期

页      面：151-161页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61139002 61501229 11547040) the Guangdong Natural Science Foundation(2016A030310051) 

主　　题：Character recognition Data mining Feature extraction Information theory 

摘      要：Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information.