Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence

Information Sciences

Volumn 366, Issue , 2016, Pages 134-149

  Zhao, Hong ^a,b   Wang, Ping ^a   Hu, Qinghua ^a  

^a TIANJIN UNIVERSITY   (China)

^b MINNAN NORMAL UNIVERSITY   (China)

Author keywords

Cost sensitive learning; Feature selection; Granular computing; Neighborhood granularity; Neighborhood rough sets

Indexed keywords

COSTS; ECONOMIC AND SOCIAL EFFECTS; FEATURE EXTRACTION; GRANULAR COMPUTING; ROUGH SET THEORY;

ADAPTIVE NEIGHBORHOOD; BACKTRACKING ALGORITHM; COST-SENSITIVE LEARNING; GRANULAR COMPUTING MODELS; MISCLASSIFICATION COSTS; NEIGHBORHOOD GRANULARITY; NEIGHBORHOOD ROUGH SETS; SAMPLING RESOLUTION;

COST BENEFIT ANALYSIS;

EID: 84973522069     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.05.025     Document Type: Article

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