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Volumn 38, Issue 10, 2005, Pages 1483-1494

Switching class labels to generate classification ensembles

Author keywords

Bagging; Boosting; Classification; Decision tree; Ensemble methods

Indexed keywords

AGGLOMERATION; ALGORITHMS; BOUNDARY CONDITIONS; DATA REDUCTION; DECISION THEORY; ERROR ANALYSIS; ITERATIVE METHODS; PARAMETER ESTIMATION; PERTURBATION TECHNIQUES; RANDOM PROCESSES; TREES (MATHEMATICS);

EID: 22844439516     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.02.020     Document Type: Article
Times cited : (85)

References (22)
  • 3
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
    • (1996) Mach. Learn. , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 4
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • L. Breiman Arcing classifiers Ann. Stat. 26 3 1998 801 849
    • (1998) Ann. Stat. , vol.26 , Issue.3 , pp. 801-849
    • Breiman, L.1
  • 5
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R. Schapire, Y. Freund, P. Bartlett, and W. Lee Boosting the margin a new explanation for the effectiveness of voting methods Ann. Stat. 12 5 1998 1651 1686
    • (1998) Ann. Stat. , vol.12 , Issue.5 , pp. 1651-1686
    • Schapire, R.1    Freund, Y.2    Bartlett, P.3    Lee, W.4
  • 6
  • 7
    • 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 Mach. Learn. 36 1-2 1999 105 139
    • (1999) Mach. Learn. , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 9
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees bagging, boosting, and randomization Mach. Learn. 40 2 2000 139 157
    • (2000) Mach. Learn. , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 10
    • 0034247206 scopus 로고    scopus 로고
    • Multiboosting: A technique for combining boosting and wagging
    • G.I. Webb Multiboosting: a technique for combining boosting and wagging Mach. Learn. 40 2 2000 159 196
    • (2000) Mach. Learn. , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 12
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman Random forests Mach. Learn. 45 1 2001 5 32
    • (2001) Mach. Learn. , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 13
    • 0037410515 scopus 로고    scopus 로고
    • Double-bagging: Combining classifiers by bootstrap aggregation
    • T. Hothorn, and B. Lausen Double-bagging: combining classifiers by bootstrap aggregation Pattern Recognition 36 6 2003 1303 1309
    • (2003) Pattern Recognition , vol.36 , Issue.6 , pp. 1303-1309
    • Hothorn, T.1    Lausen, B.2
  • 14
    • 0242515926 scopus 로고    scopus 로고
    • Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets
    • R. Bryll, R. Gutierrez-Osuna, and F. Quek Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets Pattern Recognition 36 6 2003 1291 1302
    • (2003) Pattern Recognition , vol.36 , Issue.6 , pp. 1291-1302
    • Bryll, R.1    Gutierrez-Osuna, R.2    Quek, F.3
  • 16
    • 0004140497 scopus 로고    scopus 로고
    • Out-of-bag estimation
    • Statistics Department, University of California
    • L. Breiman, Out-of-bag estimation, Technical Report, Statistics Department, University of California, 1996.
    • (1996) Technical Report
    • Breiman, L.1
  • 17
    • 0026105482 scopus 로고
    • An iterative growing and pruning algorithm for classification tree design
    • S. Gelfand, C. Ravishankar, and E. Delp An iterative growing and pruning algorithm for classification tree design IEEE Trans. Pattern Anal. Mach. Intell. 13 2 1991 138 150
    • (1991) IEEE Trans. Pattern Anal. Mach. Intell. , vol.13 , Issue.2 , pp. 138-150
    • Gelfand, S.1    Ravishankar, C.2    Delp, E.3
  • 18
    • 0034276320 scopus 로고    scopus 로고
    • Randomizing outputs to increase prediction accuracy
    • L. Breiman Randomizing outputs to increase prediction accuracy Mach. Learn. 40 3 2000 229 242
    • (2000) Mach. Learn. , vol.40 , Issue.3 , pp. 229-242
    • Breiman, L.1
  • 20
    • 0029678894 scopus 로고    scopus 로고
    • Improved use of continuous attributes in C4.5
    • J.R. Quinlan Improved use of continuous attributes in C4.5 J. Artificial Intell. Res. 4 1996 77 90
    • (1996) J. Artificial Intell. Res. , vol.4 , pp. 77-90
    • Quinlan, J.R.1
  • 22
    • 0003619255 scopus 로고    scopus 로고
    • Bias, variance, and arcing classifiers
    • Statistics Department, University of California
    • L. Breiman, Bias, variance, and arcing classifiers, Technical Report 460, Statistics Department, University of California, 1996.
    • (1996) Technical Report , vol.460
    • Breiman, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.