메뉴 건너뛰기




Volumn 2, Issue , 2007, Pages 132-139

Mining data with rare events: A case study

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CYBERNETICS;

EID: 48649107941     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2007.71     Document Type: Conference Paper
Times cited : (51)

References (18)
  • 1
    • 0004267735 scopus 로고    scopus 로고
    • Kluwer Academic Publishers, Norwell, MA, USA
    • D.W. Aha. Lazy learning. Kluwer Academic Publishers, Norwell, MA, USA, 1997.
    • (1997) Lazy learning
    • Aha, D.W.1
  • 2
    • 34547973397 scopus 로고    scopus 로고
    • The imbalanced training sample problem: Under or over sampling? In Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR/SPR'04)
    • R. Barandela, R. M. Valdovinos, J. S. Sanchez, and F. J. Ferri. The imbalanced training sample problem: Under or over sampling? In Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR/SPR'04), Lecture Notes in Computer Science 3138, (806-814), 2004.
    • (2004) Lecture Notes in Computer Science , vol.3138 , Issue.806-814
    • Barandela, R.1    Valdovinos, R.M.2    Sanchez, J.S.3    Ferri, F.J.4
  • 3
    • 0003408496 scopus 로고    scopus 로고
    • Department of Information and Computer Sciences, University of California, Irvine
    • C. Blake and C. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html, 1998. Department of Information and Computer Sciences, University of California, Irvine.
    • (1998) UCI repository of machine learning databases
    • Blake, C.1    Merz, C.2
  • 4
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 8
    • 27144501672 scopus 로고    scopus 로고
    • H. Han, W. Y. Wang, and B. H. Mao. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In In International Conference on Intelligent Computing (ICIC'05). Lecture Notes in Computer Science 3644, pages 878-887. Springer-Verlag, 2005.
    • H. Han, W. Y. Wang, and B. H. Mao. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In In International Conference on Intelligent Computing (ICIC'05). Lecture Notes in Computer Science 3644, pages 878-887. Springer-Verlag, 2005.
  • 9
    • 27144540575 scopus 로고    scopus 로고
    • Class imbalances versus small disjuncts
    • T. Jo and N. Japkowicz. Class imbalances versus small disjuncts. SIGKDD Explorations, 6(1):40-49, 2004.
    • (2004) SIGKDD Explorations , vol.6 , Issue.1 , pp. 40-49
    • Jo, T.1    Japkowicz, N.2
  • 10
    • 0036891333 scopus 로고    scopus 로고
    • Using regression trees to classify fault-prone software modules
    • T. M. Khoshgoftaar, E. B. Allen, and J. Deng. Using regression trees to classify fault-prone software modules. IEEE Trans. Reliability, 51(4):455-462, 2002.
    • (2002) IEEE Trans. Reliability , vol.51 , Issue.4 , pp. 455-462
    • Khoshgoftaar, T.M.1    Allen, E.B.2    Deng, J.3
  • 14
    • 34547995826 scopus 로고    scopus 로고
    • J. Van Hulse, T. M. Khoshgoftaar, and A. Napolitano. Experimental perspectives on learning from imbalanced data. In In Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA, June 2007.
    • J. Van Hulse, T. M. Khoshgoftaar, and A. Napolitano. Experimental perspectives on learning from imbalanced data. In In Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA, June 2007.
  • 15
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: A unifying framework
    • G. M. Weiss. Mining with rarity: A unifying framework. SIGKDD Explorations, 6(1):7-19, 2004.
    • (2004) SIGKDD Explorations , vol.6 , Issue.1 , pp. 7-19
    • Weiss, G.M.1
  • 16
    • 1442275185 scopus 로고    scopus 로고
    • Learning when training data are costly: The effect of class distribution on tree induction
    • G. M. Weiss and F. Provost. Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research, (19):315-354, 2003.
    • (2003) Journal of Artificial Intelligence Research , vol.19 , pp. 315-354
    • Weiss, G.M.1    Provost, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.