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Volumn 8, Issue , 2007, Pages 1-33

Nonlinear boosting projections for ensemble construction

Author keywords

Boosting; Classifier ensembles; Neural networks; Nonlinear projections

Indexed keywords

LEARNING SYSTEMS; NEURAL NETWORKS; PROJECTION SYSTEMS; SPURIOUS SIGNAL NOISE;

EID: 33846550644     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (72)

References (70)
  • 2
    • 0033570831 scopus 로고    scopus 로고
    • Combined 5 × 2 cv F test for comparing supervised classification learning algorithms
    • E. Alpaydin. Combined 5 × 2 cv F test for comparing supervised classification learning algorithms. Neural Computation, 11:1885-1892, 1999.
    • (1999) Neural Computation , vol.11 , pp. 1885-1892
    • Alpaydin, E.1
  • 3
    • 0003648234 scopus 로고
    • An Introduction to Multivariate Statistical Analysis
    • John Wiley & Sons, New York, 2nd edition
    • T. W. Anderson. An Introduction to Multivariate Statistical Analysis. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, New York, 2nd edition, 1984.
    • (1984) Wiley Series in Probability and Mathematical Statistics
    • Anderson, T.W.1
  • 4
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • July/August
    • E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1/2):105-142, July/August 1999.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 5
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • Breiman. Stacked regressions. Machine Learning, 24(1):49-64, 1996a.
    • (1996) Machine Learning , vol.24 , Issue.1 , pp. 49-64
    • Breiman1
  • 6
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996b.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman1
  • 7
    • 0003619255 scopus 로고    scopus 로고
    • Bias, variance, and arcing classifiers
    • Technical Report 460, Department of Statistics, University of California, Berkeley, CA
    • Breiman. Bias, variance, and arcing classifiers. Technical Report 460, Department of Statistics, University of California, Berkeley, CA, 1996c.
    • (1996)
    • Breiman1
  • 8
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • Breiman. Arcing classifiers. Annals of Statistics, 26:801-824, 1998.
    • (1998) Annals of Statistics , vol.26 , pp. 801-824
    • Breiman1
  • 9
    • 0000275022 scopus 로고    scopus 로고
    • Prediction games and arcing algorithms
    • Breiman. Prediction games and arcing algorithms. Neural Computation, 11(7):1493-1517, 1999.
    • (1999) Neural Computation , vol.11 , Issue.7 , pp. 1493-1517
    • Breiman1
  • 11
    • 10044235999 scopus 로고    scopus 로고
    • LIBSVM: A Library for Support Vector Machines
    • available at
    • Ch-Ch. Chang and Ch-J. Lin. LIBSVM: A Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/ libsvm.
    • (2001) Software
    • Chang, C.-C.1    Lin, C.-J.2
  • 12
    • 0000291808 scopus 로고    scopus 로고
    • Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification
    • K. Chen, L. Wang, and H. Chi. Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification. Journal of Pattern Recognition and Artificial Intelligence, 11 (3) :417-445, 1997.
    • (1997) Journal of Pattern Recognition and Artificial Intelligence , vol.11 , Issue.3 , pp. 417-445
    • Chen, K.1    Wang, L.2    Chi, H.3
  • 13
    • 0001920992 scopus 로고    scopus 로고
    • Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks
    • K. Cherkauer. Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks. In Working Notes of the AAAI Workshop on Integrating Multiple Learned Models, pages 15-21, 1996.
    • (1996) Working Notes of the AAAI Workshop on Integrating Multiple Learned Models , pp. 15-21
    • Cherkauer, K.1
  • 15
    • 84974722422 scopus 로고    scopus 로고
    • Diversity versus quality in classification ensembles based on feature selection
    • R. L. de Mantarás and E. Plaza, editors, Barcelona, Spain, Springer
    • P. Cunningham and J. Carney. Diversity versus quality in classification ensembles based on feature selection. In R. L. de Mantarás and E. Plaza, editors, Proceedings of the Eleventh Conference on Machine Learning ECML 2000, pages 109-116, Barcelona, Spain, 2000. Springer.
    • (2000) Proceedings of the Eleventh Conference on Machine Learning ECML 2000 , pp. 109-116
    • Cunningham, P.1    Carney, J.2
  • 18
    • 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, 40:139-157, 2000a.
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 21
    • 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):1895-1923, 1998.
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 23
    • 12144288329 scopus 로고    scopus 로고
    • Is combining classifiers with stacking better than selecting the best one?
    • S. Dzeroski and B. Zenko. Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54:255-273, 2004.
    • (2004) Machine Learning , vol.54 , pp. 255-273
    • Dzeroski, S.1    Zenko, B.2
  • 25
    • 0141921552 scopus 로고    scopus 로고
    • Online ensemble learning: An empirical study
    • A. Fern and R. Givan. Online ensemble learning: An empirical study. Machine Learning, 53: 71-109, 2003.
    • (2003) Machine Learning , vol.53 , pp. 71-109
    • Fern, A.1    Givan, R.2
  • 27
    • 0034164230 scopus 로고    scopus 로고
    • Additice logistic regression: A statistical view of boosting
    • J. Friedman, T. Hastie, and R. Tibshirani. Additice logistic regression: A statistical view of boosting. Annals of Statistics, 28(2):337-407, 2000.
    • (2000) Annals of Statistics , vol.28 , Issue.2 , pp. 337-407
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 40
    • 0003263256 scopus 로고    scopus 로고
    • Self-Organizing Maps
    • of, Springer, Berlin, third edition
    • T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, third edition, 2001.
    • (2001) Springer Series in Information Sciences , vol.30
    • Kohonen, T.1
  • 41
    • 0000670848 scopus 로고
    • Back propagation is sensitive to initial conditions
    • Richard P. Lippmann, John E. Moody, and David S. Touretzky, editors, Morgan Kaufmann Publishers, Inc
    • J. F. Kolen and J. B. Pollack. Back propagation is sensitive to initial conditions. In Richard P. Lippmann, John E. Moody, and David S. Touretzky, editors, Advances in Neural Information Processing Systems, volume 3, pages 860-867. Morgan Kaufmann Publishers, Inc., 1991.
    • (1991) Advances in Neural Information Processing Systems , vol.3 , pp. 860-867
    • Kolen, J.F.1    Pollack, J.B.2
  • 42
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • May
    • Kuncheva and C.J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2):181-207, May 2003.
    • (2003) Machine Learning , vol.51 , Issue.2 , pp. 181-207
    • Kuncheva1    Whitaker, C.J.2
  • 43
    • 0348151971 scopus 로고    scopus 로고
    • Combining classifiers: Soft computing solutions
    • S. K. Pal and A. Pal, editors, World Scientific
    • I. Kuncheva. Combining classifiers: Soft computing solutions. In S. K. Pal and A. Pal, editors, Pattern Recognition: From Classical to Modern Approaches, pages 427-451. World Scientific, 2001.
    • (2001) Pattern Recognition: From Classical to Modern Approaches , pp. 427-451
    • Kuncheva, I.1
  • 44
    • 21844454573 scopus 로고    scopus 로고
    • Error bounds for aggressive and conservative adaboost
    • Proceedings of MCS, number in, Guilford, UK
    • I. Kuncheva. Error bounds for aggressive and conservative adaboost. In Proceedings of MCS, number 2709 in Lecture Notes in Computer Science, pages 25-34, Guilford, UK, 2003.
    • (2003) Lecture Notes in Computer Science , vol.2709 , pp. 25-34
    • Kuncheva, I.1
  • 46
    • 0032822143 scopus 로고    scopus 로고
    • A comparative study of neural networks based feature extraction paradigms
    • B. Lerner, H. Guterman, M. Aladjem, and I. Dinstein. A comparative study of neural networks based feature extraction paradigms. Pattern Recognition Letters, 20(1):7-14, 1999.
    • (1999) Pattern Recognition Letters , vol.20 , Issue.1 , pp. 7-14
    • Lerner, B.1    Guterman, H.2    Aladjem, M.3    Dinstein, I.4
  • 47
    • 0034315099 scopus 로고    scopus 로고
    • Evolutionary ensembles with negative correlation learning
    • November
    • Y. Liu, X. Yao, and T. Higuchi. Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation, 4(4):380-387, November 2000.
    • (2000) IEEE Transactions on Evolutionary Computation , vol.4 , Issue.4 , pp. 380-387
    • Liu, Y.1    Yao, X.2    Higuchi, T.3
  • 49
    • 0033870982 scopus 로고    scopus 로고
    • Improved generalization through explicit optimization of margins
    • Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Machine Learning, 38:243-255, 2000.
    • (2000) Machine Learning , vol.38 , pp. 243-255
    • Mason, P.1    Bartlett, L.2    Baxter, J.3
  • 50
    • 0032661927 scopus 로고    scopus 로고
    • Using correspondence analysis to combine classifiers
    • July
    • J. Merz. Using correspondence analysis to combine classifiers. Machine Learning, 36(1):33-58, July 1999.
    • (1999) Machine Learning , vol.36 , Issue.1 , pp. 33-58
    • Merz, J.1
  • 52
    • 0032596573 scopus 로고    scopus 로고
    • Feature selection for ensembles
    • Orlando, FL, USA, American Association for Artificial Intelligence
    • W. Opitz. Feature selection for ensembles. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 379-384, Orlando, FL, USA, 1999. American Association for Artificial Intelligence.
    • (1999) Proceedings of the Sixteenth National Conference on Artificial Intelligence , pp. 379-384
    • Opitz, W.1
  • 56
    • 12844274244 scopus 로고    scopus 로고
    • Boosting as a regularized path to a maximum margin classifier
    • S. Rosset, J. Zhu, and T. Hastie. Boosting as a regularized path to a maximum margin classifier. Journal of Machine Learning Research, 5:941-073, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 941-073
    • Rosset, S.1    Zhu, J.2    Hastie, T.3
  • 57
    • 0000646059 scopus 로고
    • Learning internal representations by error propagation
    • D. Rumelhart and J. McClelland, editors, MIT Press, Cambridge, MA
    • Rumelhart, G. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. Rumelhart and J. McClelland, editors, Parallel Distributed Processing, pages 318-362. MIT Press, Cambridge, MA, 1986.
    • (1986) Parallel Distributed Processing , pp. 318-362
    • Rumelhart1    Hinton, G.2    Williams, R.J.3
  • 58
    • 0033905095 scopus 로고    scopus 로고
    • Boostexter: A boosting-based system for text categorization
    • R. E. Schapire and Y. Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39:135-168, 2000.
    • (2000) Machine Learning , vol.39 , pp. 135-168
    • Schapire, R.E.1    Singer, Y.2
  • 59
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:297-336, 1999.
    • (1999) Machine Learning , vol.37 , pp. 297-336
    • Schapire, R.E.1    Singer, Y.2
  • 60
    • 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):1651-1686, 1998.
    • (1998) Annals of Statistics , vol.26 , Issue.5 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.L.3    Lee, W.S.4
  • 61
    • 0141830857 scopus 로고    scopus 로고
    • Stopping criterion for boosting-based data reduction techniques: From binary to multiclass problems
    • Sebban, R. Nock, and S. Lallich. Stopping criterion for boosting-based data reduction techniques: from binary to multiclass problems. Journal of Machine Learning Research, 3:863-885, 2002.
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 863-885
    • Sebban1    Nock, R.2    Lallich, S.3
  • 63
    • 0042622207 scopus 로고    scopus 로고
    • A. Tsymbal, P. Cunningham, M. Pechinizkiy, and P. Puuronen. Search strategies for ensemble feature selection in medical diagnosis. In M. Krol, S. Mitra, and D. J. Lee, editors, Proceedings of the Sixteenth IEEE Symposium on Computer-Bases Medical Systems CBMS'2003, pages 124-129, The Mount Sinai School of Medicine, New York, USA, 2003. IEEE CS Press.
    • A. Tsymbal, P. Cunningham, M. Pechinizkiy, and P. Puuronen. Search strategies for ensemble feature selection in medical diagnosis. In M. Krol, S. Mitra, and D. J. Lee, editors, Proceedings of the Sixteenth IEEE Symposium on Computer-Bases Medical Systems CBMS'2003, pages 124-129, The Mount Sinai School of Medicine, New York, USA, 2003. IEEE CS Press.
  • 64
    • 0030365938 scopus 로고    scopus 로고
    • Error correlation and error reduction in ensemble classifier
    • K. Turner and J. Ghosh. Error correlation and error reduction in ensemble classifier. Connection Science, 8(3-4):385-404, 1996.
    • (1996) Connection Science , vol.8 , Issue.3-4 , pp. 385-404
    • Turner, K.1    Ghosh, J.2
  • 65
    • 0035425555 scopus 로고    scopus 로고
    • Ensemble of independent factor analyzers with application to natural image analysis
    • August
    • A. Utsugi. Ensemble of independent factor analyzers with application to natural image analysis. Neural Processing Letters, 14(1):49-60, August 2001.
    • (2001) Neural Processing Letters , vol.14 , Issue.1 , pp. 49-60
    • Utsugi, A.1
  • 66
    • 0034247206 scopus 로고    scopus 로고
    • Multiboosting: A technique for combining boosting and wagging
    • August
    • G. I. Webb. Multiboosting: A technique for combining boosting and wagging. Machine Learning, 40(2):159-196, August 2000.
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 159-196
    • Webb, G.I.1
  • 68
    • 84948152666 scopus 로고    scopus 로고
    • Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error
    • L. de Raedt and P. Flach, editors, 12th European Conference on Machine Learning ECML 2001, Springer-Verlag
    • G. Zenobi and P. Cunningham. Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In L. de Raedt and P. Flach, editors, 12th European Conference on Machine Learning (ECML 2001), LNAI 2167, pages 576-587. Springer-Verlag, 2001.
    • (2001) LNAI , vol.2167 , pp. 576-587
    • Zenobi, G.1    Cunningham, P.2
  • 69
    • 26444493144 scopus 로고    scopus 로고
    • Boosting with early stooping: Convergence and consistency
    • T. Zhang and B. Yu. Boosting with early stooping: Convergence and consistency. The Annals of Statistics, 33(4):1538-1579, 2005.
    • (2005) The Annals of Statistics , vol.33 , Issue.4 , pp. 1538-1579
    • Zhang, T.1    Yu, B.2
  • 70
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • May
    • Z-H. Zhou, J. Wu, and W. Tang. Ensembling neural networks: Many could be better than all. Artificial Intelligence, 137(1-2):239-253, May 2002.
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 239-253
    • Zhou, Z.-H.1    Wu, J.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.