A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3275-3281

  Hong, Jin Hyuk ^a   Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

Fusion method; Multi class cancer classification; Na ve Bayes classifiers; Support vector machines

Indexed keywords

CLASSIFIERS; GENE EXPRESSION; IMAGE RETRIEVAL; LEARNING SYSTEMS; NEURAL NETWORKS; RESTORATION; VECTORS;

BAYES CLASSIFIERS; BINARY CLASSIFICATIONS; CANCER CLASSIFICATIONS; CLASSIFICATION SYSTEMS; CONVENTIONAL METHODS; DATA SETS; DECOMPOSITION SCHEMES; FUSION METHOD; GENE EXPRESSION PROFILES; INDIVIDUAL CLASSIFIERS; INFORMATIVE GENES; MAJORITY VOTING; MULTI-CLASS CANCER CLASSIFICATION; MULTIPLE OUTPUTS; NOVEL METHODS; PEARSON CORRELATIONS; SUPPORT VECTORS;

SUPPORT VECTOR MACHINES;

ACCURACY; AUTOMATED PATTERN RECOGNITION; BAYESIAN LEARNING; BIOINFORMATICS; CANCER CLASSIFICATION; CLASSIFIER; CONFERENCE PAPER; CONTROLLED STUDY; CORRELATION COEFFICIENT; GENE EXPRESSION PROFILING; INTERMETHOD COMPARISON; PRIORITY JOURNAL; PROBABILITY; SUPPORT VECTOR MACHINE;

EID: 56549128359     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.04.033     Document Type: Conference Paper

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