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Volumn , Issue , 2011, Pages 1146-1151

Calculating feature weights in naive Bayes with Kullback-Leibler measure

Author keywords

Classification; Feature weighting; Naive Bayes

Indexed keywords

DATA MINING APPLICATIONS; DATA SETS; EMPIRICAL RESULTS; FEATURE WEIGHT; FEATURE WEIGHTING; KULLBACK-LEIBLER MEASURE; NAIVE BAYES; NAIVE BAYESIAN;

EID: 84863137187     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.29     Document Type: Conference Paper
Times cited : (112)

References (15)
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    • Wrappers for feature subset selection
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    • Kohavi, R.1    John, G.H.2
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    • Feature selection for the naive bayesian classifier using decision trees
    • C. A. Ratanamahatana and D. Gunopulos. Feature selection for the naive bayesian classifier using decision trees. Applied Artificial Intelligence, 17(5-6):475-487, 2003.
    • (2003) Applied Artificial Intelligence , vol.17 , Issue.5-6 , pp. 475-487
    • Ratanamahatana, C.A.1    Gunopulos, D.2
  • 15
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    • A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms
    • Dietrich Wettschereck, David W. Aha, and Takao Mohri. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11:273-314, 1997.
    • (1997) Artificial Intelligence Review , vol.11 , pp. 273-314
    • Wettschereck, D.1    Aha, D.W.2    Mohri, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.