메뉴 건너뛰기




Volumn 16, Issue 5, 2012, Pages 777-801

IIvotes ensemble for imbalanced data

Author keywords

ensemble classifiers; Imbalanced data; informed re sampling; Ivotes adaptive ensemble; SPIDER method

Indexed keywords

ENSEMBLE CLASSIFIERS; IMBALANCED DATA; IVOTES ADAPTIVE ENSEMBLE; RESAMPLING; SPIDER METHOD;

EID: 84868684788     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2012-0551     Document Type: Article
Times cited : (10)

References (49)
  • 1
    • 27144531570 scopus 로고    scopus 로고
    • A study of the behaviour of several methods for balancing machine learning training data
    • G. Batista, R. Prati and M. Monard, A study of the behaviour of several methods for balancing machine learning training data, ACM SIGKDD Explorations Newsletter 6(1) (2004), 20-29
    • (2004) ACM SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 20-29
    • Batista, G.1    Prati, R.2    Monard, M.3
  • 2
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • E. Bauer and R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning 36(1) (1999), 105-139.
    • (1999) Machine Learning , vol.36 , Issue.1 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 5
    • 0032634129 scopus 로고    scopus 로고
    • Pasting small votes for classification in large databases and on-line
    • L. Breiman, Pasting small votes for classification in large databases and on-line, Machine Learning 36 (1999), 85-103.
    • (1999) Machine Learning , vol.36 , pp. 85-103
    • Breiman, L.1
  • 7
    • 9444297357 scopus 로고    scopus 로고
    • SMOTEBoost: Improving Prediction of the Minority Class in Boosting
    • Knowledge Discovery in Databases: PKDD 2003
    • N. Chawla, A. Lazarevic, L. Hall and K. Bowyer, SMOTEBoost: Improving prediction of the minority class in boosting, In Proceedings of the Principles of Knowledge Discovery in Databases, PKDD2003 (2003), 107-119. (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
  • 8
    • 37949004300 scopus 로고    scopus 로고
    • Data mining for imbalanced datasets: An overview, Chapter
    • O. Maimon and L. Rokach eds Springer-Verlag
    • N. Chawla, Data mining for imbalanced datasets: An overview, Chapter in O. Maimon and L. Rokach eds, The Data Mining and Knowledge Discovery Handbook, Springer-Verlag, 2005, pp. 853-867.
    • (2005) The Data Mining and Knowledge Discovery Handbook , pp. 853-867
    • Chawla, N.1
  • 9
    • 34249966007 scopus 로고
    • The CN2 induction algorithm
    • P. Clark and T. Niblett, The CN2 induction algorithm, Machine Learning 3(4) (1989), 261-283.
    • (1989) Machine Learning , vol.3 , Issue.4 , pp. 261-283
    • Clark, P.1    Niblett, T.2
  • 11
    • 0031269184 scopus 로고    scopus 로고
    • On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
    • P. Domingos and M. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning 29(2) (1997), 103-130. (Pubitemid 127510035)
    • (1997) Machine Learning , vol.29 , Issue.2-3 , pp. 103-130
    • Domingos, P.1    Pazzani, M.2
  • 12
    • 34250080806 scopus 로고
    • A weighted nearest neighbour algorithm for learning with symbolic features
    • S. Cost and S. Salzberg, A weighted nearest neighbour algorithm for learning with symbolic features, Machine Learning Journal 10(1) (1993), 1213-1228.
    • (1993) Machine Learning Journal , vol.10 , Issue.1 , pp. 1213-1228
    • Cost, S.1    Salzberg, S.2
  • 18
    • 0035151960 scopus 로고    scopus 로고
    • Three discretization methods for rule induction
    • DOI 10.1002/1098-111X(200101)16:1<29::AID-INT4>3.0.CO;2-0
    • J.W. Grzymala-Busse and J. Stefanowski, Three approaches to numerical attribute discretization for rule induction, International Journal of Intelligent Systems 16(1) (2001), 29-38. (Pubitemid 32089955)
    • (2001) International Journal of Intelligent Systems , vol.16 , Issue.1 , pp. 29-38
    • Grzymala-Busse, J.W.1    Stefanowski, J.2
  • 19
    • 27144479454 scopus 로고    scopus 로고
    • Learning from imbalanced data sets with boosting and data generation: The DataBoost im approach
    • H. Guo and H. Victor, Learning from imbalanced data sets with boosting and data generation: The DataBoost IM approach, ACM SIGKDD Explorations Newsletter 6(1) (2004), 30-39.
    • (2004) ACM SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 30-39
    • Guo, H.1    Victor, H.2
  • 21
    • 0242370859 scopus 로고    scopus 로고
    • Attribute Interactions in Medical Data Analysis
    • Artificial Intelligence in Medicine
    • A. Jakulin, I. Bratko, D. Smrke, J. Demsar and B. Zupan, Attribute interactions in medical data analysis, M. Dojat, E. Keravnou and Barahona, eds, Proceedings of the 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003 LNCS 2780 (2003), 229-238. (Pubitemid 37342897)
    • (2003) Lecture Notes in Computer Science , Issue.2780 , pp. 229-238
    • Jakulin, A.1    Bratko, I.2    Smrke, D.3    Demsar, J.4    Zupan, B.5
  • 35
    • 9444270977 scopus 로고    scopus 로고
    • Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior
    • MICAI 2004: Advances in Artificial Intelligence Third Mexican International Conference on Artificial Intelligence Mexico City, Mexico, April 26-30, 2004 Proceedings
    • R. Prati, G. Batista and M. Monard, Class imbalance versus class overlapping: An analysis of a learning system behaviour, In Proceedings of 3rd Mexican International Conference on Artificial Intelligence (2004), 312-321. (Pubitemid 38716795)
    • (2004) Lecture Notes in Computer Science , Issue.2972 , pp. 312-321
    • Prati, R.C.1    Batista, G.E.A.P.A.2    Monard, M.C.3
  • 38
    • 0037806811 scopus 로고    scopus 로고
    • The boosting approach to machine learning: An overview
    • D.D. Denison, M.H. Hansen, C. Holmes, B. Mallick and B. Yu, eds Springer-Verlag
    • R.E. Schapire, The boosting approach to machine learning: An overview, D.D. Denison, M.H. Hansen, C. Holmes, B. Mallick and B. Yu, eds, Nonlinear Estimation and Classification, Springer-Verlag, 2003.
    • (2003) Nonlinear Estimation and Classification
    • Schapire, R.E.1
  • 40
  • 43
    • 52949096003 scopus 로고    scopus 로고
    • Improving rule based classifiers induced by MODLEM by selective pre-processing of im-balanced data
    • J. Stefanowski and S. Wilk, Improving rule based classifiers induced by MODLEM by selective pre-processing of im-balanced data, In Proceedings of the RSKD Workshop at ECML/PKDD (2007), 54-65.
    • (2007) Proceedings of the RSKD Workshop at ECML/PKDD , pp. 54-65
    • Stefanowski, J.1    Wilk, S.2
  • 45
    • 67651243664 scopus 로고    scopus 로고
    • Extending rule-based classifiers to improve recognition of imbalanced classes
    • Z.W. Ras and A. Dardzinska, eds
    • J. Stefanowski and Sz. Wilk, Extending rule-based classifiers to improve recognition of imbalanced classes, Z.W. Ras and A. Dardzinska, eds, Advances in Data Management, Studies in Computational Intelligence 223 (2009), 131-154.
    • (2009) Advances in Data Management, Studies in Computational Intelligence , vol.223 , pp. 131-154
    • Stefanowski, J.1    Wilk, Sz.2
  • 47
    • 77957583037 scopus 로고    scopus 로고
    • Boosting support vector machines for imbalanced data sets
    • B. Wang and N. Japkowicz, Boosting support vector machines for imbalanced data sets, Knowledge and Information Systems 25(1) (2010), 1-20.
    • (2010) Knowledge and Information Systems , vol.25 , Issue.1 , pp. 1-20
    • Wang, B.1    Japkowicz, N.2
  • 48
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: A unifying framework
    • G.M. Weiss, Mining with rarity: A unifying framework, ACM SIGKDD Explorations Newsletter 6(1) (2004), 7-19.
    • (2004) ACM SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 7-19
    • Weiss, G.M.1
  • 49
    • 0343081513 scopus 로고    scopus 로고
    • Reduction techniques for instance-based learning algorithms
    • DOI 10.1023/A:1007626913721
    • D.R. Wilson and T. Martinez, Reduction techniques for instance-based learning algorithms, Machine Learning Journal 38 (2000), 257-286. (Pubitemid 30572450)
    • (2000) Machine Learning , vol.38 , Issue.3 , pp. 257-286
    • Randall Wilson, D.1    Martinez, T.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.