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Volumn 53, Issue 4, 2012, Pages 584-607

The impact of diversity on the accuracy of evidential classifier ensembles

Author keywords

Belief functions; Diversity; Ensemble learning; Triplet evidence structure

Indexed keywords

BELIEF FUNCTION; CLASSIFIER ENSEMBLES; DEMPSTER'S RULE; DEMSPTER'S RULE; DIFFERENT ORDER; DIVERSITY; DIVERSITY MEASURE; ENSEMBLE LEARNING; ENSEMBLE PERFORMANCE; GENERALIZATION ERROR; MASS FUNCTIONS;

EID: 84858795085     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2011.12.011     Document Type: Article
Times cited : (98)

References (37)
  • 1
    • 22044453925 scopus 로고    scopus 로고
    • The combination of text classifiers using reliability indicators
    • DOI 10.1023/B:INRT.0000048491.59134.94
    • P.N. Bennett, S.T. Dumais, and E. Horvitz The combination of text classifiers using reliability indicators Journal Information Retrieval 8 1 2005 67 100 (Pubitemid 40965679)
    • (2005) Information Retrieval , vol.8 , Issue.1 , pp. 67-100
    • Bennett, P.N.1    Dumais, S.T.2    Horvitz, E.3
  • 3
    • 35348924230 scopus 로고    scopus 로고
    • Methods for person identification on a pressure-sensitive floor: Experiments with multiple classifiers and reject option
    • DOI 10.1016/j.inffus.2006.11.003, PII S156625350600114X, Applications of Ensemble Methods
    • J. Suutala, and J. Roning Methods for person identification on a pressure-sensitive floor: experiments with multiple classifiers and reject option Information Fusion 9 1 2008 21 40 (Pubitemid 47589062)
    • (2008) Information Fusion , vol.9 , Issue.1 , pp. 21-40
    • Suutala, J.1    Roning, J.2
  • 5
    • 79551683321 scopus 로고    scopus 로고
    • A belief function classifier based on information provided by noisy and dependent features
    • P. Monney, M. Chan, and P. Romberg A belief function classifier based on information provided by noisy and dependent features International Journal of Approximate Reasoning 52 3 2011 335 352
    • (2011) International Journal of Approximate Reasoning , vol.52 , Issue.3 , pp. 335-352
    • Monney, P.1    Chan, M.2    Romberg, P.3
  • 8
    • 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 2003 181 207
    • (2003) Machine Learning , vol.51 , pp. 181-207
    • Kuncheva, L.1    Whitaker, C.J.2
  • 10
    • 85054435084 scopus 로고
    • Neural network ensembles, cross validation, and active learning
    • G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.) Denver,Colorado, USA
    • A. Krogh, J. Vedelsby, Neural network ensembles, cross validation, and active learning, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.), Advances in Neural Information Processing Systems 7, NIPS Conference, Denver,Colorado, USA, 1995, pp. 231-238.
    • (1995) Advances in Neural Information Processing Systems 7, NIPS Conference , pp. 231-238
    • Krogh, A.1    Vedelsby, J.2
  • 11
    • 10444259853 scopus 로고    scopus 로고
    • Creating diversity in ensembles using artificial data
    • P. Melville, and R.J. Mooney Creating diversity in ensembles using artificial data Information Fusion 6 2005 99 111
    • (2005) Information Fusion , vol.6 , pp. 99-111
    • Melville, P.1    Mooney, R.J.2
  • 12
    • 33749018252 scopus 로고    scopus 로고
    • An analysis of diversity measures
    • DOI 10.1007/s10994-006-9449-2
    • E.K. Tang, P.N. Suganthan, and X. Yao An analysis of diversity measures Machine Learning 65 1 2006 247 271 (Pubitemid 44451200)
    • (2006) Machine Learning , vol.65 , Issue.1 , pp. 247-271
    • Tang, E.K.1    Suganthan, P.N.2    Yao, X.3
  • 13
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee Boosting the margin: a new explanation for the effectiveness of voting methods The Annals of Statistics 26 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
  • 15
    • 34249314710 scopus 로고    scopus 로고
    • Analyzing the combination of conflicting belief functions
    • Ph. Smets Analyzing the combination of conflicting belief functions Information Fusion 8 2007 387 412
    • (2007) Information Fusion , vol.8 , pp. 387-412
    • Smets, Ph.1
  • 16
    • 33745854779 scopus 로고    scopus 로고
    • Analyzing the degree of conflict among belief functions
    • DOI 10.1016/j.artint.2006.05.002, PII S0004370206000658
    • W. Liu Analyzing the degree of conflict among belief functions Artificial Intelligence 170 11 2006 909 924 (Pubitemid 44037538)
    • (2006) Artificial Intelligence , vol.170 , Issue.11 , pp. 909-924
    • Liu, W.1
  • 17
    • 42049121425 scopus 로고    scopus 로고
    • An efficient triplet-based algorithm for evidential reasoning
    • Y. Bi An efficient triplet-based algorithm for evidential reasoning International Journal of Intelligent Systems 23 4 2008 1 34
    • (2008) International Journal of Intelligent Systems , vol.23 , Issue.4 , pp. 1-34
    • Bi, Y.1
  • 18
    • 50649109430 scopus 로고    scopus 로고
    • The combination of multiple classifiers using an evidential approach
    • Y. Bi, J. Guan, and D. Bell The combination of multiple classifiers using an evidential approach Artificial Intelligence 17 2008 1731 1751
    • (2008) Artificial Intelligence , vol.17 , pp. 1731-1751
    • Bi, Y.1    Guan, J.2    Bell, D.3
  • 19
    • 33947274593 scopus 로고    scopus 로고
    • Combining multiple classifiers using Dempster's rule for text categorization
    • DOI 10.1080/08839510601170887, PII 772951271
    • Y. Bi, D. Bell, H. Wang, G. Guo, and J. Guan Combining multiple classifiers using Dempster's rule for text categorization Applied Artificial Intelligence 21 3 2007 211 239 (Pubitemid 46417501)
    • (2007) Applied Artificial Intelligence , vol.21 , Issue.3 , pp. 211-239
    • Bi, Y.1    Bell, D.2    Wang, H.3    Guo, G.4    Guan, J.5
  • 20
    • 84858793529 scopus 로고    scopus 로고
    • Measuring impact of diversity of classifiers on the accuracy of evidential ensemble classifiers
    • Y. Bi, and S. Wu Measuring impact of diversity of classifiers on the accuracy of evidential ensemble classifiers IPMU 1 2010 238 247
    • (2010) IPMU , vol.1 , pp. 238-247
    • Bi, Y.1    Wu, S.2
  • 21
    • 0030122443 scopus 로고    scopus 로고
    • EDM: A general framework for Data Mining based on Evidence Theory
    • DOI 10.1016/0169-023X(95)00038-T
    • S.S. Anand, D. Bell, and J.G. Hughes EDM: a general framework for data mining based on evidence theory Data Knowledge Engineering 18 3 1996 189 223 (Pubitemid 126375298)
    • (1996) Data and Knowledge Engineering , vol.18 , Issue.3 , pp. 189-223
    • Anand, S.S.1    Bell, D.A.2    Hughes, J.G.3
  • 22
    • 0023311178 scopus 로고
    • On the Dempster-Shafer framework and new combination rules
    • R.R. Yager On the Dempster-Shafer framework and new combination rules Information Science 41 1987 93 137
    • (1987) Information Science , vol.41 , pp. 93-137
    • Yager, R.R.1
  • 24
    • 13844249111 scopus 로고
    • Representation, independence, and combination of evidence in the Dempster-Shafer theory
    • R.R. Yager, J. Kacprzyk, M. Fedrizzi, John Wiley & Sons New York
    • L. Zhang Representation, independence, and combination of evidence in the Dempster-Shafer theory R.R. Yager, J. Kacprzyk, M. Fedrizzi, Advances in the Dempster-Shafer Theory of Evidence 1994 John Wiley & Sons New York 51 69
    • (1994) Advances in the Dempster-Shafer Theory of Evidence , pp. 51-69
    • Zhang, L.1
  • 25
    • 0022093334 scopus 로고
    • Method for managing evidential reasoning in a hierarchical hypothesis space
    • DOI 10.1016/0004-3702(85)90064-5
    • J. Gordon, and E.H. Shortliffe A method for managing evidential reasoning in a hierarchical hypothesis space Artificial Intelligence 26 1985 323 357 (Pubitemid 15524384)
    • (1985) Artificial Intelligence , vol.26 , Issue.3 , pp. 323-357
    • Gordon Jean1    Shortliffe Edward, H.2
  • 26
    • 37149055072 scopus 로고    scopus 로고
    • Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence
    • DOI 10.1016/j.artint.2007.05.008, PII S0004370207001063
    • T. Denoeux Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence Artificial Intelligence 172 2-3 2008 234 264 (Pubitemid 350256729)
    • (2008) Artificial Intelligence , vol.172 , Issue.2-3 , pp. 234-264
    • Denoeux, T.1
  • 27
    • 79551686194 scopus 로고    scopus 로고
    • Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules
    • B. Quost, M.-H. Masson, and T. Denoeux Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules International Journal of Approximate Reasoning 52 3 2011 353 374
    • (2011) International Journal of Approximate Reasoning , vol.52 , Issue.3 , pp. 353-374
    • Quost, B.1    Masson, M.-H.2    Denoeux, T.3
  • 30
    • 0002872346 scopus 로고    scopus 로고
    • Bias plus variance decomposition for zero-one loss functions
    • L. Saitta, Morgan Kaufman
    • R. Kohavi, and D. Wolpert Bias plus variance decomposition for zero-one loss functions L. Saitta, Machine Learning: Proc. 13th International Conference 1996 Morgan Kaufman 275 283
    • (1996) Machine Learning: Proc. 13th International Conference , pp. 275-283
    • Kohavi, R.1    Wolpert, D.2
  • 31
    • 84970216868 scopus 로고
    • The reliability of dichotomous judgments: Unequal numbers of judgments per subject
    • J.L. Fleiss, and J. Cuzick The reliability of dichotomous judgments: unequal numbers of judgments per subject Applied Psychological Measurement 3 1979 537 542
    • (1979) Applied Psychological Measurement , vol.3 , pp. 537-542
    • Fleiss, J.L.1    Cuzick, J.2
  • 35
    • 0001562581 scopus 로고    scopus 로고
    • Linear and order statistics combiners for pattern classification
    • A. Sharkey (Ed.)
    • K. Tumer and J. Ghosh, Linear and order statistics combiners for pattern classification, in: A. Sharkey (Ed.), Combining Artificial Neural Nets, 1999, pp. 127-161.
    • (1999) Combining Artificial Neural Nets , pp. 127-161
    • Tumer, K.1    Ghosh, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.