An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme

Knowledge-Based Systems

Volumn 158, Issue , 2018, Pages 81-93

  Bi, Jingjun ^a   Zhang, Chongsheng ^a  

^a HENAN UNIVERSITY   (China)

Author keywords

Classification algorithms; Decomposition methods for multi class data; Multi class imbalance learning; Multi class imbalanced data classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); CODES (SYMBOLS); LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

CLASS IMBALANCE LEARNING; CLASSIFICATION ALGORITHM; DECOMPOSITION METHODS; EMPIRICAL - COMPARISONS; ERROR CORRECTING OUTPUT CODE; MULTI-CLASS IMBALANCED DATUM; OVERALL ACCURACIES; STATE-OF-THE-ART ALGORITHMS;

LEARNING ALGORITHMS;

EID: 85048303100     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2018.05.037     Document Type: Article

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