메뉴 건너뛰기




Volumn 193, Issue , 2012, Pages 1-21

Supervised subspace projections for constructing ensembles of classifiers

Author keywords

Classification; Ensembles of classifiers; Subspace methods; Supervised projections

Indexed keywords

BASE LEARNERS; BIAS AND VARIANCE; CLASS LABELS; ENSEMBLE METHODS; ENSEMBLES OF CLASSIFIERS; PROJECTION METHOD; RANDOM SUBSPACE METHOD; RANDOM SUBSPACES; SUB-SPACE METHODS; SUBSPACE PROJECTION; SUPERVISED PROJECTIONS; TESTING ERRORS; UCI MACHINE LEARNING REPOSITORY;

EID: 84857789335     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2011.06.023     Document Type: Article
Times cited : (36)

References (49)
  • 2
    • 33847673255 scopus 로고    scopus 로고
    • Learning to classify e-mail
    • DOI 10.1016/j.ins.2006.12.005, PII S0020025506003707, Including Special Issue on Hybrid Intelligent Systems
    • I. Koprinska, J. Poon, J. Clark, and J. Chan Learning to classify e-mail Information Sciences 177 2007 2167 2187 (Pubitemid 46356315)
    • (2007) Information Sciences , vol.177 , Issue.10 , pp. 2167-2187
    • Koprinska, I.1    Poon, J.2    Clark, J.3    Chan, J.4
  • 3
    • 60349123276 scopus 로고    scopus 로고
    • Incremental construction of classifier and discriminant ensembles
    • A. Ula, M. Semerci, O.T. Yildiz, and E. Alpaydin Incremental construction of classifier and discriminant ensembles Information Sciences 179 2009 1298 1318
    • (2009) Information Sciences , vol.179 , pp. 1298-1318
    • Ula, A.1    Semerci, M.2    Yildiz, O.T.3    Alpaydin, E.4
  • 4
    • 0033207482 scopus 로고    scopus 로고
    • Combining predictors: Comparison of five meta machine learning methods
    • DOI 10.1016/S0020-0255(99)00052-3
    • J. Hansen Combining predictors: comparison of five meta machine learning methods Information Sciences 119 1-2 1999 91 105 (Pubitemid 30544699)
    • (1999) Information sciences , vol.119 , Issue.1-2 , pp. 91-105
    • Hansen, J.V.1
  • 8
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140 (Pubitemid 126724382)
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 10
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R.E. Schapire, Y. Freund, P.L. Bartlett, and W.S. Lee Boosting the margin: a new explanation for the effectiveness of voting methods Annals of Statistics 26 5 1998 1651 1686 (Pubitemid 128376902)
    • (1998) Annals of Statistics , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4
  • 11
    • 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 1/2 1999 105 142
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 12
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • L. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Machine Learning 51 2 2003 181 207
    • (2003) Machine Learning , vol.51 , Issue.2 , pp. 181-207
    • Kuncheva, L.1    Whitaker, C.J.2
  • 13
    • 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 Machine Learning 40 2000 139 157
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 14
  • 15
    • 84957007471 scopus 로고    scopus 로고
    • Bagging and the Random Subspace Method for Redundant Feature Spaces
    • Multiple Classifier Systems
    • M. Skurichina, R.P.W. Duin, Bagging and the random subspace method for redundant feature spaces, in: J. Kittler, R. Poli (Eds.), Proceedings of the Second International Workshop on Multiple Classifier Systems MCS 2001, Cambridge, UK, 2001, pp. 1-10. (Pubitemid 33303208)
    • (2001) Lecture Notes in Computer Science , Issue.2096 , pp. 1-10
    • Skurichina, M.1    Duin, R.P.W.2
  • 17
    • 77956614386 scopus 로고    scopus 로고
    • Constructing ensembles of classifiers using supervised projection methods based on misclassified instances
    • N. García-Pedrajas, and C. García-Osorio Constructing ensembles of classifiers using supervised projection methods based on misclassified instances Expert Systems with Applications 38 1 2010 343 359
    • (2010) Expert Systems with Applications , vol.38 , Issue.1 , pp. 343-359
    • García-Pedrajas, N.1    García-Osorio, C.2
  • 25
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • DOI 10.1126/science.290.5500.2323
    • S.T. Roweis, and L.K. Saul Nonlinear dimensionality reduction by locally linear embedding Science 290 5500 2000 2323 2326 (Pubitemid 32041578)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 28
    • 22844435049 scopus 로고    scopus 로고
    • Incremental locally linear embedding
    • DOI 10.1016/j.patcog.2005.04.006, PII S0031320305001792
    • O. Kouropteva, O. Okun, and M. Pietikäinen Incremental locally linear embedding Pattern Recognition 38 10 2005 1764 1767 (Pubitemid 41037108)
    • (2005) Pattern Recognition , vol.38 , Issue.10 , pp. 1764-1767
    • Kouropteva, O.1    Okun, O.2    Pietikainen, M.3
  • 29
    • 31544473466 scopus 로고    scopus 로고
    • Incremental nonlinear dimensionality reduction by manifold learning
    • DOI 10.1109/TPAMI.2006.56
    • M.H.C. Law, and A.K. Jain Incremental nonlinear dimensionality reduction by manifold learning IEEE Transactions on Pattern Analysis and Machine Intelligence 28 3 2006 377 391 (Pubitemid 43159634)
    • (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.28 , Issue.3 , pp. 377-391
    • Law, M.H.C.1    Jain, A.K.2
  • 31
    • 67349098382 scopus 로고    scopus 로고
    • Embedding new data points for manifold learning via coordinate propagation
    • S. Xiang, F. Nie, Y. Song, C. Zhang, and C. Zhang Embedding new data points for manifold learning via coordinate propagation Knowledge and Information Systems 19 2 2009 159 184
    • (2009) Knowledge and Information Systems , vol.19 , Issue.2 , pp. 159-184
    • Xiang, S.1    Nie, F.2    Song, Y.3    Zhang, C.4    Zhang, C.5
  • 36
    • 67149088596 scopus 로고    scopus 로고
    • Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy
    • X.-Z. Wang, and C.-R. Dong Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy IEEE Transactions on Fuzzy Systems 2009 556 567
    • (2009) IEEE Transactions on Fuzzy Systems , pp. 556-567
    • Wang, X.-Z.1    Dong, C.-R.2
  • 40
    • 0000259511 scopus 로고    scopus 로고
    • Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
    • T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 7 1998 1895 1923 (Pubitemid 128463689)
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 41
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demsar, J.1
  • 42
    • 0034247206 scopus 로고    scopus 로고
    • MultiBoosting: A technique for combining boosting and wagging
    • G.I. Webb MultiBoosting: a technique for combining boosting and wagging Machine Learning 40 2 2000 159 196
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 45
    • 2442688288 scopus 로고    scopus 로고
    • Genetic programming in classifying large-scale data: An ensemble method
    • Y. Zhang, and S. Bhattacharyya Genetic programming in classifying large-scale data: an ensemble method Information Sciences 163 2004 85 101
    • (2004) Information Sciences , vol.163 , pp. 85-101
    • Zhang, Y.1    Bhattacharyya, S.2
  • 48
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • L. Breiman Stacked regressions Machine Learning 24 1 1996 49 64 (Pubitemid 126724379)
    • (1996) Machine Learning , vol.24 , Issue.1 , pp. 49-64
    • Breiman, L.1
  • 49
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1 - Loss, and the curse-of-dimensionality
    • J.H. Friedman On bias, variance, 0/1 - loss, and the curse-of- dimensionality Data Mining and Knowledge Discovery 1 1997 55 77
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 55-77
    • Friedman, J.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.