메뉴 건너뛰기




Volumn 6119 LNAI, Issue PART 2, 2010, Pages 488-499

Generating diverse ensembles to counter the problem of class imbalance

Author keywords

[No Author keywords available]

Indexed keywords

CLASS IMBALANCE; CLASS IMBALANCE PROBLEMS; DATA MINING COMMUNITY; DATA SETS; DIVERSE ENSEMBLES; IMBALANCED DATA-SETS; RANDOM SUBSPACES; SAMPLING METHOD;

EID: 79956326532     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13672-6_46     Document Type: Conference Paper
Times cited : (23)

References (24)
  • 2
    • 80053403826 scopus 로고    scopus 로고
    • Ensemble methods in machine learning
    • Kittler, J., Roli, F. (eds.) MCS 2000. Springer, Heidelberg
    • Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
    • (2000) LNCS , vol.1857 , pp. 1-15
    • Dietterich, T.G.1
  • 3
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L.: Bagging predictors. Machine Learning 24(2), 123-140 (1996) (Pubitemid 126724382)
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 5
    • 9444297357 scopus 로고    scopus 로고
    • SMOTEBoost: Improving prediction of the minority class in boosting
    • Knowledge Discovery in Databases: PKDD 2003
    • Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: Smoteboost: improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107-119. Springer, Heidelberg (2003) (Pubitemid 37231089)
    • (2003) Lecture Notes in Computer Science , Issue.2838 , pp. 107-119
    • Chawla, N.V.1    Lazarevic, A.2    Hall, L.O.3    Bowyer, K.W.4
  • 6
    • 84878083672 scopus 로고    scopus 로고
    • Exploratory under-sampling for class-imbalance learning
    • DOI 10.1109/ICDM.2006.68, 4053136, Proceedings - Sixth International Conference on Data Mining, ICDM 2006
    • Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory under-sampling for class-imbalance learning. In: ICDM '06: Proceedings of the Sixth International Conference on Data Mining, Washington, DC, USA, pp. 965-969. IEEE Computer Society, Los Alamitos (2006) (Pubitemid 47485889)
    • (2007) Proceedings - IEEE International Conference on Data Mining, ICDM , pp. 965-969
    • Liu, X.-Y.1    Wu, J.2    Zhou, Z.-H.3
  • 7
    • 27144479454 scopus 로고    scopus 로고
    • Learning from imbalanced data sets with boosting and data generation: The databoost-im approach
    • ACM, New York
    • Guo, H., Viktor,H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. In: SIGKDD Explorations, pp. 30-39. ACM, New York (2004)
    • (2004) SIGKDD Explorations , pp. 30-39
    • Guo, H.1    Viktor, H.L.2
  • 10
    • 56049126929 scopus 로고    scopus 로고
    • Learning decision trees for unbalanced data
    • Daelemans, W., et al. (eds.) ECML PKDD 2008, Part I. Springer, Heidelberg
    • Cieslak, D.A., Chawla, N.V.: Learning decision trees for unbalanced data. In: Daelemans, W., et al. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 241-256. Springer, Heidelberg (2008)
    • (2008) LNCS (LNAI) , vol.5211 , pp. 241-256
    • Cieslak, D.A.1    Chawla, N.V.2
  • 14
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006) (Pubitemid 43022939)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demsar, J.1
  • 15
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles
    • Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181-207 (2003)
    • (2003) Machine Learning , vol.51 , pp. 181-207
    • Kuncheva, L.I.1    Whitaker, C.J.2
  • 16
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40(19), 139-157 (2000)
    • (2000) Machine Learning , vol.40 , Issue.19 , pp. 139-157
    • Dietterich, T.G.1
  • 19
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • DOI 10.1023/A:1010933404324
    • Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001) (Pubitemid 32933532)
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 20
    • 52649160312 scopus 로고    scopus 로고
    • Roughly balanced bagging for imbalanced data
    • SIAM, Philadelphia
    • Hido, S., Kashima, H.: Roughly balanced bagging for imbalanced data. In: Statistical Analysis and Data Mining, pp. 143-152. SIAM, Philadelphia (2008)
    • (2008) Statistical Analysis and Data Mining , pp. 143-152
    • Hido, S.1    Kashima, H.2
  • 22
    • 1442275185 scopus 로고    scopus 로고
    • Learning when training data are costly: The effect of class distribution on tree induction
    • Weiss, G.M., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artifical Intelligent Research 19, 315-354 (2003) (Pubitemid 41525924)
    • (2003) Journal of Artificial Intelligence Research , vol.19 , pp. 315-354
    • Weiss, G.M.1    Provost, F.2
  • 23
    • 27144531570 scopus 로고    scopus 로고
    • A study of the behavior of several methods for balancing machine learning training data
    • Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6, 20-29 (2004)
    • (2004) SIGKDD Explorations , vol.6 , pp. 20-29
    • Batista, G.E.A.P.A.1    Prati, R.C.2    Monard, M.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.