메뉴 건너뛰기




Volumn 218, Issue 1, 2012, Pages 202-210

Mining axiomatic fuzzy set association rules for classification problems

Author keywords

AFS fuzzy logic; Classification; Data mining; Fuzzy association rules; Knowledge acquisition

Indexed keywords

DATA TYPE; FUZZY ASSOCIATION RULE; IMBALANCED CLASS; INTERPRETABILITY; INTERPRETABLE RULES; MINE ASSOCIATION RULES; NEW MODEL; RULE GENERATION;

EID: 83955162952     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2011.04.022     Document Type: Article
Times cited : (34)

References (37)
  • 5
    • 42549111230 scopus 로고    scopus 로고
    • Toward a theory of granular computing for human-centered information processing
    • DOI 10.1109/TFUZZ.2007.905912
    • A. Bargiela, and W. Pedrycz Toward a theory of granular computing for human-centered information processing IEEE Transactions on Fuzzy Systems 16 2 2008 320 330 (Pubitemid 351586034)
    • (2008) IEEE Transactions on Fuzzy Systems , vol.16 , Issue.2 , pp. 320-330
    • Bargiela, A.1    Pedrycz, W.2
  • 6
    • 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
  • 7
    • 33749543645 scopus 로고    scopus 로고
    • A new approach to classification based on association rule mining
    • DOI 10.1016/j.dss.2005.03.005, PII S0167923605000576
    • G. Chen, H. Liu, L. Yu, Q. Wei, and X. Zhang A new approach to classification based on association rule mining Decision Support Systems 42 2006 674 689 (Pubitemid 44537467)
    • (2006) Decision Support Systems , vol.42 , Issue.2 , pp. 674-689
    • Chen, G.1    Liu, H.2    Yu, L.3    Wei, Q.4    Zhang, X.5
  • 8
    • 56649103776 scopus 로고    scopus 로고
    • Mining fuzzy association rules from questionnaire data
    • Y.L. Chen, and C.H. Weng Mining fuzzy association rules from questionnaire data Knowledge-Based Systems 22 2009 46 56
    • (2009) Knowledge-Based Systems , vol.22 , pp. 46-56
    • Chen, Y.L.1    Weng, C.H.2
  • 9
    • 9644252808 scopus 로고    scopus 로고
    • Elicitation of fuzzy association rules from positive and negative examples
    • M.D. Cock, C. Cornelis, and E.E. Kerre Elicitation of fuzzy association rules from positive and negative examples Fuzzy Sets and Systems 149 2005 73 85
    • (2005) Fuzzy Sets and Systems , vol.149 , pp. 73-85
    • Cock, M.D.1    Cornelis, C.2    Kerre, E.E.3
  • 11
    • 17744368529 scopus 로고    scopus 로고
    • On the representation, measurement, and discovery of fuzzy associations
    • DOI 10.1109/TFUZZ.2004.840130
    • D. Dubois, H. Prade, and T. Sudkamp On the representation, measurement, and discovery of fuzzy associations IEEE Transactions on Fuzzy Systems 13 2 2005 250 262 (Pubitemid 40573239)
    • (2005) IEEE Transactions on Fuzzy Systems , vol.13 , Issue.2 , pp. 250-262
    • Dubois, D.1    Prade, H.2    Sudkamp, T.3
  • 12
    • 77950370185 scopus 로고    scopus 로고
    • Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment
    • M. Dunbar, J.M. Murray, L.A. Cysique, B.J. Brew, and V. Jeyakumar Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment European Journal of Operational Research 206 2 2010 470 478
    • (2010) European Journal of Operational Research , vol.206 , Issue.2 , pp. 470-478
    • Dunbar, M.1    Murray, J.M.2    Cysique, L.A.3    Brew, B.J.4    Jeyakumar, V.5
  • 13
    • 33847172327 scopus 로고    scopus 로고
    • Clustering by passing messages between data points
    • DOI 10.1126/science.1136800
    • B.J. Frey, and D. Dueck Clustering by passing messages between data points Science 315 2007 972 976 (Pubitemid 46281181)
    • (2007) Science , vol.315 , Issue.5814 , pp. 972-976
    • Frey, B.J.1    Dueck, D.2
  • 14
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R.C. Holte Very simple classification rules perform well on most commonly used datasets Machine Learning 11 1993 63 91
    • (1993) Machine Learning , vol.11 , pp. 63-91
    • Holte, R.C.1
  • 15
    • 35649016085 scopus 로고    scopus 로고
    • An overview of mining fuzzy association rules
    • H. Bustince, F. Herrera, J. Montero, Springer Berlin, Heidelberg
    • T. Hong, and Y. Lee An overview of mining fuzzy association rules H. Bustince, F. Herrera, J. Montero, Studies in Fuzziness and Soft Computing vol. 220 2008 Springer Berlin, Heidelberg 397 410
    • (2008) Studies in Fuzziness and Soft Computing , vol.220 , pp. 397-410
    • Hong, T.1    Lee, Y.2
  • 16
    • 84883745941 scopus 로고    scopus 로고
    • Fuzzy Association Rules: Semantic Issues and Quality Measures
    • Computational Intelligence Theory and Applications
    • E. Hüllermeier Fuzzy association rules: Semantic issues and quality measures B. Reusch, Computational Intelligence: Theory and Applications; Proceeding seventh Fuzzy Days Dortmund Lecture Notes in Computer Science vol. 2206 2001 Springer Berlin 380 391 (Pubitemid 33359515)
    • (2001) Lecture Notes in Computer Science , Issue.2206 , pp. 380-391
    • Hullermeier, E.1
  • 18
    • 0000545946 scopus 로고    scopus 로고
    • Improvements to Platt's SMO algorithm for SVM classifier design
    • DOI 10.1162/089976601300014493
    • S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, and K.R.K. Murthy Improvements to Platt's SMO algorithm for SVM classifier design Neural Computation 13 3 2001 637 649 (Pubitemid 33595014)
    • (2001) Neural Computation , vol.13 , Issue.3 , pp. 637-649
    • Keerthi, S.S.1    Shevade, S.K.2    Bhattacharyya, C.3    Murthy, K.R.K.4
  • 19
    • 34548515525 scopus 로고    scopus 로고
    • On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid
    • DOI 10.1016/j.ejor.2006.10.059, PII S0377221706011465
    • P. Lenca, P. Meyer, B. Vaillant, and S. Lallich On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid European Journal of Operational Research 184 2008 610 626 (Pubitemid 47374386)
    • (2008) European Journal of Operational Research , vol.184 , Issue.2 , pp. 610-626
    • Lenca, P.1    Meyer, P.2    Vaillant, B.3    Lallich, S.4
  • 20
    • 78149313084 scopus 로고    scopus 로고
    • CMAR: Accurate and efficient classification based on multiple class-association rules
    • IEEE Computer Society San Jose, California
    • W. Li, J. Han, and J. Pei CMAR: Accurate and efficient classification based on multiple class-association rules ICDM 2001 2001 IEEE Computer Society San Jose, California 369 376
    • (2001) ICDM 2001 , pp. 369-376
    • Li, W.1    Han, J.2    Pei, J.3
  • 21
    • 34548033746 scopus 로고    scopus 로고
    • Fuzzy feature selection based on min-max learning rule and extension matrix
    • DOI 10.1016/j.patcog.2007.06.007, PII S0031320307002725
    • Y. Li, and Z.F. Wu Fuzzy feature selection based on min-max learning rule and extension matrix Pattern Recognition 41 2008 217 226 (Pubitemid 47284220)
    • (2008) Pattern Recognition , vol.41 , Issue.1 , pp. 217-226
    • Li, Y.1    Wu, Z.-F.2
  • 22
    • 84948104699 scopus 로고    scopus 로고
    • Integrating classification and association rule mining
    • New York
    • Liu B.; Hsu W.; Ma Y.; 1998. Integrating classification and association rule mining. In: KDD98, New York, pp. 80-86.
    • (1998) KDD98 , pp. 80-86
    • Liu, B.1    Hsu, W.2    Ma, Y.3
  • 25
    • 31144448615 scopus 로고    scopus 로고
    • Using simulated annealing to optimize the feature selection problem in marketing applications
    • DOI 10.1016/j.ejor.2004.09.010, PII S0377221704005892, Feature Cluster: Heuristic and Stochastic Methods in Optimization
    • R. Meiri, and J. Zahavi Using simulated annealing to optimize the feature selection problem in marketing applications European Journal of Operational Research 171 2006 842 858 (Pubitemid 43132950)
    • (2006) European Journal of Operational Research , vol.171 , Issue.3 , pp. 842-858
    • Meiri, R.1    Zahavi, J.2
  • 28
    • 38649127201 scopus 로고    scopus 로고
    • Compact fuzzy association rule-based classifier
    • DOI 10.1016/j.eswa.2007.04.005, PII S0957417407001339
    • F.P. Pach, A. Gyenesei, and J. Abonyi Compact fuzzy association rule-based classifier Expert Systems with Applications 34 2008 2406 2416 (Pubitemid 351173751)
    • (2008) Expert Systems with Applications , vol.34 , Issue.4 , pp. 2406-2416
    • Pach, F.P.1    Gyenesei, A.2    Abonyi, J.3
  • 31
    • 0033906947 scopus 로고    scopus 로고
    • Fuzzy rule based classification with FeatureSelector and modified threshold accepting
    • DOI 10.1016/S0377-2217(99)00090-9
    • V. Ravi, and H.J. Zimmermann Fuzzy rule based classifiation with FeatureSelector and modified threshold accepting European Journal of Operational Research 123 2000 16 28 (Pubitemid 30575849)
    • (2000) European Journal of Operational Research , vol.123 , Issue.1 , pp. 16-28
    • Ravi, V.1    Zimmermann, H.-J.2
  • 32
    • 33846028551 scopus 로고    scopus 로고
    • Learning fuzzy rules with their implication operators
    • DOI 10.1016/j.datak.2006.01.007, PII S0169023X06000206
    • M. Serrurier, D. Dubois, H. Prade, and T. Sudkamp Learning fuzzy rules with their implication operators Data and Knowledge Engineering 60 26 2007 71 89 (Pubitemid 46053592)
    • (2007) Data and Knowledge Engineering , vol.60 , Issue.1 , pp. 71-89
    • Serrurier, M.1    Dubois, D.2    Prade, H.3    Sudkamp, T.4
  • 33
    • 0343040701 scopus 로고    scopus 로고
    • Fuzzy-pattern recognition for automatic detection of different teeth substances
    • J. Strackeljan, D. Behr, and T. Kocher Fuzzy pattern recognition for automatic detection of different teeth substances Fuzzy Sets and Systems 85 1997 275 286 (Pubitemid 127699049)
    • (1997) Fuzzy Sets and Systems , vol.85 , Issue.2 , pp. 275-286
    • Strackeljan, J.1    Behr, D.2    Kocher, T.3
  • 34
    • 9644270327 scopus 로고    scopus 로고
    • Examples, counterexamples, and measuring fuzzy associations
    • T. Sudkamp Examples, counterexamples, and measuring fuzzy associations Fuzzy Sets and Systems 149 2005 57 71
    • (2005) Fuzzy Sets and Systems , vol.149 , pp. 57-71
    • Sudkamp, T.1
  • 35
    • 77951139898 scopus 로고    scopus 로고
    • A discrete particle swarm optimization method for feature selection in binary classification problems
    • A. Unler, and A. Murat A discrete particle swarm optimization method for feature selection in binary classification problems European Journal of Operational Research 206 3 2010 528 539
    • (2010) European Journal of Operational Research , vol.206 , Issue.3 , pp. 528-539
    • Unler, A.1    Murat, A.2
  • 37
    • 19544366904 scopus 로고    scopus 로고
    • CPAR: Classiffication based on predictive association rules
    • San Fransisco, CA
    • Yin, X.; Han, J.; 2003. CPAR: Classiffication based on predictive association rules. In: SDM'03, San Fransisco, CA, pp. 331-335.
    • (2003) SDM'03 , pp. 331-335
    • Yin, X.1    Han, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.