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Volumn 1, Issue , 2006, Pages 476-481

Thresholding for making classifiers cost-sensitive

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COST EFFECTIVENESS; LEARNING ALGORITHMS; THRESHOLD LOGIC;

EID: 33750689152     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (145)

References (20)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting and variants
    • Bauer, E., and Kohavi, R. 1999. An empirical comparison of voting classification algorithms: bagging, boosting and variants, Machine Learning, 36(1/2):105-139.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Brieman, L. 1996. Bagging predictors. Machine Learning, 24:123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Brieman, L.1
  • 15
    • 0000865580 scopus 로고
    • Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm
    • Turney, P.D. 1995. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm. Journal of Artificial Intelligence Research 2:369-409.
    • (1995) Journal of Artificial Intelligence Research , vol.2 , pp. 369-409
    • Turney, P.D.1
  • 17
    • 1442275185 scopus 로고    scopus 로고
    • Learning when training data are costly: The effect of class distribution on tree induction
    • Weiss, G., and Provost, F. 2003. Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research 19: 315-354.
    • (2003) Journal of Artificial Intelligence Research , vol.19 , pp. 315-354
    • Weiss, G.1    Provost, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.