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Volumn 3138, Issue , 2004, Pages 762-770

Cost-based classifier evaluation for imbalanced problems

Author keywords

[No Author keywords available]

Indexed keywords

COSTS;

EID: 33845306768     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27868-9_83     Document Type: Article
Times cited : (8)

References (11)
  • 3
    • 0017017305 scopus 로고
    • On the choice of smoothing parameters for parzen estimators of probability density functions
    • R.P.W. Duin. On the choice of smoothing parameters for parzen estimators of probability density functions. IEEE Trans. Computing, 25:1175-1179, 1976.
    • (1976) IEEE Trans. Computing , vol.25 , pp. 1175-1179
    • Duin, R.P.W.1
  • 5
    • 1942421135 scopus 로고    scopus 로고
    • The geometry of roc space: Understanding machine learning metrics through roc isometrics
    • P. Flach. The geometry of roc space: understanding machine learning metrics through roc isometrics. ICML-2003 Washington DC, pages 194-201, 2003.
    • (2003) ICML-2003 Washington DC , pp. 194-201
    • Flach, P.1
  • 7
    • 34147123577 scopus 로고
    • Linear decision functions, with application to pattern recognition
    • W. Highleyman. Linear decision functions, with application to pattern recognition. Proc. IRE, 49:31-48, 1961.
    • (1961) Proc. IRE , vol.49 , pp. 31-48
    • Highleyman, W.1
  • 8
    • 0001972236 scopus 로고    scopus 로고
    • Addressing the curse of imbalanced data sets: One-sided sampling
    • July
    • M. Kubat and S. Matwin. Addressing the curse of imbalanced data sets: One-sided sampling. Proceedings, 14th ICML, Nashville, pages 179-186, July 1997.
    • (1997) Proceedings, 14th ICML, Nashville , pp. 179-186
    • Kubat, M.1    Matwin, S.2
  • 9
    • 0018079655 scopus 로고
    • Basic principles of roc analysis
    • G. Metz. Basic principles of roc analysis. Seminars in Nuclear Medicine, 3(4), 1978.
    • (1978) Seminars in Nuclear Medicine , vol.3 , Issue.4
    • Metz, G.1
  • 10
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification for imprecise environments
    • F. Provost and T. Fawcett. Robust classification for imprecise environments. Machine Learning, 42:203-231, 2001.
    • (2001) Machine Learning , vol.42 , pp. 203-231
    • Provost, F.1    Fawcett, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.