A Novel Single‐Feature and Synergetic‐Features Selection Method by Using ISE‐Based KDE and Random Permutation. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Zhang, Jingxiang; Wang, Shitong
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 1(2016)
• Page Start: 114
• Page End: 120
• Publication Date: 2016-01-01
• Subjects: Feature selection -- Integrated squared error -- Random permutation; feature selection -- mean square error methods; synergetic feature selection criterion -- supervised feature selection -- pattern recognition -- robust criterion -- integrated square error -- random permutation -- ISE‐based KDE -- kernel density estimation; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.01.018
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : The Integrated square error (ISE), as a robust criterion for measuring the difference of densities between two datasets, have been commonly used in pattern recognition. In this paper, two different criteria for evaluating candidate feature subsets are investigated: first, a novel supervised feature selection criterion based on ISE and random permutation of a single feature is proposed, which presents a feature ranking criterion to measure the importance of each feature by computing the ISE over the feature space. Second, a synergetic feature selection criterion is developed. Experimental results on synthetic and real data set show the superior or at least comparable performance compared with existing feature selection algorithms.