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Volumn 52, Issue 9, 2011, Pages 1409-1432

Classification systems based on rough sets under the belief function framework

Author keywords

Belief functions; Generalization distribution table; Rough sets; Uncertainty and classification

Indexed keywords

ATTRIBUTE SELECTION; BASIC CONCEPTS; BELIEF FUNCTION; CLASSIFICATION APPROACH; CLASSIFICATION METHODS; CLASSIFICATION PROCESS; CLASSIFICATION SYSTEM; CONSTRUCTION PROCEDURES; DECISION ATTRIBUTE; DECISION RULES; GENERALIZATION DISTRIBUTION TABLES; REAL-WORLD DATABASE; ROUGH SET; TIME COMPLEXITY; TWO CLASSIFICATION; UNCERTAIN DATAS; UNCERTAINTY AND CLASSIFICATION; WEB USAGE;

EID: 80955142768     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2011.08.002     Document Type: Conference Paper
Times cited : (30)

References (43)
  • 2
    • 0031206656 scopus 로고    scopus 로고
    • Approximation algorithms and decision making in the Dempster-Shafer theory of evidence -An empirical study
    • PII S0888613X97000133
    • M. Bauer Approximations algorithm and decision making in the Dempster-Shafer theory of evidence - an empirical study IJAR 17 2-3 1997 217 237 (Pubitemid 127399852)
    • (1997) International Journal of Approximate Reasoning , vol.17 , Issue.2-3 , pp. 217-237
    • Bauer, M.1
  • 4
    • 0010623928 scopus 로고    scopus 로고
    • A new distance between two bodies of evidence
    • DOI 10.1016/S1566-2535(01)00026-4, PII S1566253501000264
    • E. Bosse, A.L. Jousseleme, and D. Grenier A new distance between two bodies of evidence Information Fusion 2 2001 91 101 (Pubitemid 33632552)
    • (2001) Information Fusion , vol.2 , Issue.2 , pp. 91-101
    • Jousselme, A.-L.1    Grenier, D.2    Bosse, E.3
  • 6
    • 0029307876 scopus 로고
    • A k-nearest neighbor classification rule based on Dempster-Shafer theory
    • T. Denoeux A k-nearest neighbor classification rule based on Dempster-Shafer theory IEEE Transactions on Systems, Man and Cybernetics 25 5 1995 804 813
    • (1995) IEEE Transactions on Systems, Man and Cybernetics , vol.25 , Issue.5 , pp. 804-813
    • Denoeux, T.1
  • 9
    • 84957713178 scopus 로고    scopus 로고
    • Probabilistic rough induction: The GDS-RS methodology and algorithms
    • Z.W. Ras, A. Skowron, Springer Berlin
    • J.Z. Dong, N. Zhong, and S. Ohsuga Probabilistic rough induction: the GDS-RS methodology and algorithms Z.W. Ras, A. Skowron, Foundations of Intelligent Systems 1999 Springer Berlin 621 629
    • (1999) Foundations of Intelligent Systems , pp. 621-629
    • Dong, J.Z.1    Zhong, N.2    Ohsuga, S.3
  • 10
    • 0002588839 scopus 로고
    • Putting rough sets and fuzzy sets together
    • R. Slowinski, Kluwer Dordrecht
    • D. Dubois Putting rough sets and fuzzy sets together R. Slowinski, Intelligent Decision Support 1992 Kluwer Dordrecht 203 232
    • (1992) Intelligent Decision Support , pp. 203-232
    • Dubois, D.1
  • 11
    • 0035501830 scopus 로고    scopus 로고
    • Belief decision trees: Theoretical foundations
    • DOI 10.1016/S0888-613X(01)00045-7, PII S0888613X01000457
    • Z. Elouedi, K. Mellouli, and P. Smets Belief decision trees: theoretical foundations International Journal of Approximate Reasoning 28 2-3 2001 91 124 (Pubitemid 32833458)
    • (2001) International Journal of Approximate Reasoning , vol.28 , Issue.2-3 , pp. 91-124
    • Elouedi, Z.1    Mellouli, K.2    Smets, P.3
  • 13
    • 0742307275 scopus 로고    scopus 로고
    • Assessing sensor reliability for multisensor data fusion within the transferable belief model
    • Z. Elouedi, K. Mellouli, and P. Smets Assessing sensor reliability for multisensor data fusion within the transferable belief model IEEE Transactions on Systems, Man and Cybernetics 34 1 2004 782 787
    • (2004) IEEE Transactions on Systems, Man and Cybernetics , vol.34 , Issue.1 , pp. 782-787
    • Elouedi, Z.1    Mellouli, K.2    Smets, P.3
  • 14
    • 80955137747 scopus 로고    scopus 로고
    • Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables
    • T.F. Fan, C.J. Liau, and D.R. Liu Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables International Journal of Approximate Reasoning 52 9 2011 1283 1297
    • (2011) International Journal of Approximate Reasoning , vol.52 , Issue.9 , pp. 1283-1297
    • Fan, T.F.1    Liau, C.J.2    Liu, D.R.3
  • 16
  • 18
    • 34548834354 scopus 로고    scopus 로고
    • Rough set approaches to rule induction from incomplete data
    • Perugia, Italy, July 4-9
    • J.W. Grzymala-Busse, S. Siddhaye, Rough set approaches to rule induction from incomplete data, in: Proceedings of the IPMU'2004, Perugia, Italy, July 4-9, vol. 2, 2004, pp. 923-930.
    • (2004) Proceedings of the IPMU'2004 , vol.2 , pp. 923-930
    • Grzymala-Busse, J.W.1    Siddhaye, S.2
  • 20
    • 79551682436 scopus 로고    scopus 로고
    • Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data
    • P. Maji, and S. Paul Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data International Journal of Approximate Reasoning 52 3 2011 408 426
    • (2011) International Journal of Approximate Reasoning , vol.52 , Issue.3 , pp. 408-426
    • Maji, P.1    Paul, S.2
  • 21
    • 0033728781 scopus 로고    scopus 로고
    • Combining belief functions when evidence conflicts
    • C.K. Murphy Combining belief functions when evidence conflicts Decision Support Systems 29 2000 1 9
    • (2000) Decision Support Systems , vol.29 , pp. 1-9
    • Murphy, C.K.1
  • 27
    • 0002395767 scopus 로고
    • The discernibility matrices and functions in information systems
    • R. Slowinski, Kluwer Academic Publishers Boston, MA
    • A. Skowron, and C. Rauszer The discernibility matrices and functions in information systems R. Slowinski, Intelligent Decision Support 1992 Kluwer Academic Publishers Boston, MA 331 362
    • (1992) Intelligent Decision Support , pp. 331-362
    • Skowron, A.1    Rauszer, C.2
  • 29
    • 0028406490 scopus 로고
    • The transferable belief model
    • P. Smets, and R. Kennes The transferable belief model Artificial Intelligence 66 2 1994 191 234
    • (1994) Artificial Intelligence , vol.66 , Issue.2 , pp. 191-234
    • Smets, P.1    Kennes, R.2
  • 30
    • 0001099497 scopus 로고    scopus 로고
    • The transferable belief model for quantified belief representation
    • D.M. Gabbay, P. Smets, Kluwer Dordrecht, The Netherlands
    • P. Smets The transferable belief model for quantified belief representation D.M. Gabbay, P. Smets, Handbook of Defeasible Reasoning and Uncertainty Management Systems vol. 1 1998 Kluwer Dordrecht, The Netherlands 207 301
    • (1998) Handbook of Defeasible Reasoning and Uncertainty Management Systems , vol.1 , pp. 207-301
    • Smets, P.1
  • 31
    • 0031999096 scopus 로고    scopus 로고
    • Application of the transferable belief model to diagnostic problems
    • P. Smets Application of belief transferable belief model to diagnostic problems International Journal of Intelligent Systems 13 2-3 1998 127 157 (Pubitemid 128591038)
    • (1998) International Journal of Intelligent Systems , vol.13 , Issue.2-3 , pp. 127-157
    • Smets, P.1
  • 32
    • 79955557689 scopus 로고    scopus 로고
    • Core-generating approximate minimum entropy discretization for rough set feature selection in pattern classification
    • D. Tian, X.J. Zeng, and J. Keane Core-generating approximate minimum entropy discretization for rough set feature selection in pattern classification International Journal of Approximate Reasoning 52 6 2011 863 880
    • (2011) International Journal of Approximate Reasoning , vol.52 , Issue.6 , pp. 863-880
    • Tian, D.1    Zeng, X.J.2    Keane, J.3
  • 36
    • 77954878266 scopus 로고    scopus 로고
    • Rule discovery process based on rough sets under the belief function framework
    • LNAI 6178
    • S. Trabelsi, Z. Elouedi, P. Lingras, Rule discovery process based on rough sets under the belief function framework, IPMU 2010, LNAI 6178, 2010, pp. 726-736.
    • (2010) IPMU 2010 , pp. 726-736
    • Trabelsi, S.1    Elouedi, Z.2    Lingras, P.3
  • 37
    • 77949291930 scopus 로고    scopus 로고
    • Heuristic method for attribute selection from partially uncertain data using rough sets
    • S. Trabelsi, and Z. Elouedi Heuristic method for attribute selection from partially uncertain data using rough sets International Journal of General Systems 39 3 2010 271 290
    • (2010) International Journal of General Systems , vol.39 , Issue.3 , pp. 271-290
    • Trabelsi, S.1    Elouedi, Z.2
  • 39
    • 3142775061 scopus 로고    scopus 로고
    • On aggregating belief decision trees
    • DOI 10.1016/j.inffus.2004.01.001, PII S1566253504000132
    • P. Vannoorenberghe On aggregating belief decision trees Information Fusion 5 2 2004 179 188 (Pubitemid 38930718)
    • (2004) Information Fusion , vol.5 , Issue.3 , pp. 179-188
    • Vannoorenberghe, P.1
  • 40
    • 0035416447 scopus 로고    scopus 로고
    • Using rough sets with heuristics for feature selection
    • DOI 10.1023/A:1011219601502
    • N. Zhong, J.Z. Dong, and S. Ohsuga Using rough sets with heuristics for feature selection Journal of Intelligent Information Systems 16 3 2001 199 214 (Pubitemid 32886812)
    • (2001) Journal of Intelligent Information Systems , vol.16 , Issue.3 , pp. 199-214
    • Zhong, N.1    Dong, J.2    Ohsuga, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.