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Volumn , Issue , 2008, Pages 184-190

On the relationships among various diversity measures in multiple classifier systems

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; DECODING; LEARNING SYSTEMS; PARALLEL ALGORITHMS; PARALLEL PROCESSING SYSTEMS;

EID: 49149094299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/I-SPAN.2008.46     Document Type: Conference Paper
Times cited : (13)

References (18)
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  • 5
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    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • Kuncheva, L.L, Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51 (2003) 181-207
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    • Kuncheva, L.L.1    Whitaker, C.J.2
  • 6
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    • Kuncheva, L.: That elusive diversity in classifier ensembles. In Lopez, F.J.P., Campilho, A.C., de la Blanca, N.P., Sanfeliu, A., eds.: IbPRIA. 2652 of Lecture Notes in Computer Science., Springer (2003) 1126-1138
    • Kuncheva, L.: That elusive diversity in classifier ensembles. In Lopez, F.J.P., Campilho, A.C., de la Blanca, N.P., Sanfeliu, A., eds.: IbPRIA. Volume 2652 of Lecture Notes in Computer Science., Springer (2003) 1126-1138
  • 8
    • 85054435084 scopus 로고
    • Neural network ensembles, cross validation, and active learning
    • Tesauro, G, Touretzky, D.S, Leen, T.K, eds, MIT Press
    • Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In Tesauro, G., Touretzky, D.S., Leen, T.K., eds.: Advances in Neural Information Processing Systems. Volume 7., MIT Press (1995) 231-238
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 231-238
    • Krogh, A.1    Vedelsby, J.2
  • 9
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    • Linear and order statistics combiners for pattern classification
    • Sharkey, A, ed, Springer-Verlag
    • Turner, K., Ghosh, J.: Linear and order statistics combiners for pattern classification. In Sharkey, A., ed.: Combining Artificial Neural Nets. Springer-Verlag (1999) 127-162
    • (1999) Combining Artificial Neural Nets , pp. 127-162
    • Turner, K.1    Ghosh, J.2
  • 10
    • 17444420350 scopus 로고    scopus 로고
    • Comparing rank and score combination methods for data fusion in information retrieval
    • Hsu, D.F., Taksa, I.: Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval 8(3) (2005) 449-480
    • (2005) Information Retrieval , vol.8 , Issue.3 , pp. 449-480
    • Hsu, D.F.1    Taksa, I.2
  • 12
    • 32044473249 scopus 로고    scopus 로고
    • Using diversity of errors for selecting members of a committee classifier
    • Aksela, M., Laaksonen, J.: Using diversity of errors for selecting members of a committee classifier. Pattern Recognition 39 (2006) 608-623
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  • 13
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    • Diversity measures for multiple classifier system analysis and design
    • Windeatt, T.: Diversity measures for multiple classifier system analysis and design. Information Fusion 6 (2005) 21-36
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • Dietterich, T.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning 40 (2000) 139-157
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  • 16
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    • On the performance-diversity relationship for majority voting in classifier ensembles
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    • Chung, Y.S., Hsu, D.F., Tang, C.Y.: On the performance-diversity relationship for majority voting in classifier ensembles, in Multiple Classifier Systems, Lecture Notes in Computer Science, J. Kittler, F. Roli, and M. Haindl, eds, V. 4472, Springer (2007), 407-420.
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    • Ruta, D., Gabrys, B.: Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In: Proc. Second International Workshop on. Multiple Classifier Systems. Volume 2096 of Lecture Notes in Computer Science., Springer-Verlag (2001) 399-408
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    • Random forests
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.