Feature selection for classification with class-separability strategy and data envelopment analysis

Neurocomputing

Volumn 166, Issue , 2015, Pages 172-184

  Zhang, Yishi ^a,b   Yang, Chao ^a   Yang, Anrong ^a   Xiong, Chan ^a   Zhou, Xingchi ^a,c   Zhang, Zigang ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Class separability strategy; Classification; Data envelopment analysis; Feature selection; Super efficiency

Indexed keywords

DATA ENVELOPMENT ANALYSIS; EFFICIENCY; FEATURE EXTRACTION; ITERATIVE METHODS; REDUNDANCY;

CLASS LABELS; CLASS SEPARABILITY; CLASSIFICATION ACCURACY; CONDITIONAL DEPENDENCE; FEATURE SELECTION METHODS; FIVE STATE; RELEVANCE AND REDUNDANCIES; SUPER EFFICIENCY;

CLASSIFICATION (OF INFORMATION);

ARTICLE; CLASS SEPARABILITY STRATEGY; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA ENVELOPMENT ANALYSIS; FEASIBILITY STUDY; FEATURE SELECTION; INTERMETHOD COMPARISON; MACHINE LEARNING; MEASUREMENT ACCURACY; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 84931575672     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.081     Document Type: Article

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