An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets

Neurocomputing

Volumn 146, Issue , 2014, Pages 125-136

  Antonelli, Michela ^a   Ducange, Pietro ^a   Marcelloni, Francesco ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Fuzzy rule based classifiers; Genetic and evolutionary fuzzy systems; Imbalanced datasets

Indexed keywords

EVOLUTIONARY ALGORITHMS; FUZZY INFERENCE; FUZZY RULES; MEMBERSHIP FUNCTIONS; FUZZY SETS; FUZZY SYSTEMS;

AREA UNDER THE ROC CURVE; EVOLUTIONARY FUZZY SYSTEMS; EVOLUTIONARY GENERATION; FUZZY RULE-BASED CLASSIFIER; IMBALANCED DATA-SETS; MULTI-OBJECTIVE EVOLUTIONARY; NON-PARAMETRIC TEST; STATE-OF-THE-ART APPROACH;

CLASSIFICATION (OF INFORMATION);

ARTICLE; CLASSIFICATION ALGORITHM; DATA ANALYSIS; EVOLUTIONARY ALGORITHM; FUZZY SYSTEM; GENETIC ALGORITHM; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROCESS DESIGN; PROCESS DEVELOPMENT; PROCESS MODEL; PROCESS OPTIMIZATION; RECEIVER OPERATING CHARACTERISTIC; SAMPLE SIZE; SENSITIVITY ANALYSIS; SYSTEM ANALYSIS; CALCULATION; COMPARATIVE EFFECTIVENESS; CONTROLLED STUDY; EVOLUTIONARY FUZZY CLASSIFIER; EXPERIMENTAL STUDY; IMBALANCED DATASET; MULTIOBJECTIVE EVOLUTIONARY LEARNING MODEL; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICAL PARAMETERS;

EID: 84906951435     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.070     Document Type: Article

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