Boosting Naïve Bayes text classification using uncertainty-based selective sampling

Neurocomputing

Volumn 67, Issue 1-4 SUPPL., 2005, Pages 403-410

  Kim, Han Joon ^a   Kim, Je Uk ^b   Ra, Young Gook ^a  

^a University of Seoul   (South Korea)

^b Daewoo Information Systems Company   (South Korea)

Author keywords

Active learning; Boosting; Machine learning; Na ve Bayes learning; Selective sampling; Uncertainty

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; UNCERTAIN SYSTEMS;

BOOSTING ALGORITHM; CLASSIFICATION ACCURACY; SELECTIVE SAMPLING; TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLASSIFICATION; COMPUTER PROGRAM; DATA ANALYSIS; DOCUMENT EXAMINATION; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; PRIORITY JOURNAL; PROBABILITY; SAMPLING; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS;

EID: 21744460586     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.09.003     Document Type: Article

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