Open AccessA Novel Single‐Feature and Synergetic‐Features Selection Method by Using ISE‐Based KDE and Random Permutation
Author(s) -
Zhang Jingxiang,
Wang Shitong
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.01.018
Subject(s) - feature selection , feature (linguistics) , pattern recognition (psychology) , ranking (information retrieval) , random permutation , permutation (music) , artificial intelligence , computer science , set (abstract data type) , mathematics , data mining , philosophy , linguistics , physics , geometry , acoustics , block (permutation group theory) , programming language
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.