A proposed method for learning rule weights in fuzzy rule-based classification systems

Fuzzy Sets and Systems

Volumn 159, Issue 4, 2008, Pages 449-459

  Jahromi, M Zolghadri ^a   Taheri, M ^a  

^a SHIRAZ UNIVERSITY   (Iran)

Author keywords

Data mining; Fuzzy systems; Pattern recognition; Rule weight learning

Indexed keywords

DATA MINING; FUZZY SYSTEMS; LEARNING SYSTEMS; PATTERN RECOGNITION;

CLASSIFICATION SYSTEMS; RULE WEIGHT LEARNING;

FUZZY RULES;

EID: 37349070770     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2007.08.007     Document Type: Article

References (26)

1. Abe S., and Lan M.S. A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Systems 3 1 (1995) 18-28
2. Abe S., and Thawonmas R. A fuzzy classifier with ellipsoidal regions. IEEE Trans. Fuzzy Systems 5 3 (1997) 358-368
3. Agrawal R., and Srikant R. Fast algorithms for mining association rules. Proc. 20th Internat. Conf. on Very Large Databases (1994) 487-499
4. Casillas J., Cordón O., Del Jesus M.J., and Herrera F. Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Inform. Sci. 136 1-4 (2001) 135-157
5. Chung Hu Y., and Hshiung Tzeng G. Elicitation of classification rules by fuzzy data mining. Engrg. Appl. of Artificial Intelligence 16 7-8 (2003) 709-716
6. Cordon O., del Jesus M.J., and Herrera F. A proposal on reasoning methods in fuzzy rule-based classification systems. Internat. J. Approx. Reason. 20 (1999) 21-45
7. De Cock M., Cornelis C., and Kerre E.E. Elicitation of fuzzy association rules from positive and negative examples. Fuzzy Sets and Systems 149 1 (2005) 73-85
8. T. Fawcett, ROC graphs: notes and practical considerations for researchers, Technical Report HPL-2003-4, HP Labs, 2003.
9. Gómez-Skarmeta A.F., Valdés M., Jiménez F., and Marín-Blázquez J.G. Approximative fuzzy rules approaches for classification with hybrid-GA techniques. Inform. Sci. 136 1-4 (2001) 193-214
10. Gonzalez A., and Perez R. SLAVE: a genetic learning system based on an iterative approach. IEEE Trans. Fuzzy Systems 7 2 (1999) 176-191
11. Ishibuchi H., Nakashima T., and Morisawa T. Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets and Systems 103 2 (1999) 223-238
12. Ishibuchi H., Nakashima T., and Murata T. Three-objective genetics-based machine learning for linguistic rule extraction. Inform. Sci. 136 1-4 (2001) 109-133
13. Ishibuchi H., Nozaki K., and Tanaka H. Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52 1 (1992) 21-32
14. Ishibuchi H., and Yamamoto T. Comparison of heuristic criteria for fuzzy rule selection in classification problems. Fuzzy Optimization and Decision Making 3 2 (2004) 119-139
15. Ishibuchi H., and Yamamoto T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems 141 1 (2004) 59-88
16. Ishibuchi H., and Yamamoto T. Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Systems 13 4 (2005) 428-435
17. Lachiche N., and Flach P. Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. Proc. 20th Internat. Conf. on Machine Learning (ICML'03) (2003) 416-423
18. Mansoori E.G., Zolghadri M.J., and Katebi S.D. A weighting function for improving fuzzy classification systems performance. Fuzzy Sets and Systems 158 5 (2007) 583-591
19. Mitra S., and Kuncheva L.I. Improving classification performance using fuzzy MLP and two-level selective partitioning of the feature space. Fuzzy Sets and Systems 70 1 (1995) 1-13
20. Nauck D., and Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems 89 3 (1997) 277-288
21. Nauck D., and Kruse R. How the learning of rule weights affects the interpretability of fuzzy systems. Proc. 7th IEEE Internat. Conf. on Fuzzy Systems (1998) 1235-1240
22. Nozaki K., Ishibuchi H., and Tanaka H. Adaptive fuzzy rule-based classification systems. IEEE Trans. Fuzzy Systems 4 3 (1996) 238-250
23. Roubos J.A., Setnes M., and Abonyi J. Learning fuzzy classification rules from labeled data. Inform. Sci. 150 1-2 (2003) 77-93
24. Sánchez L., Couso I., Corrales J.A., Cordón O., Del Jesus M.J., and Herrera F. Combining GP operators with SA search to evolve fuzzy rule based classifiers. Inform. Sci. 136 1-4 (2001) 175-191
25. Uebele V., Abe S., and Lan M.S. A neural-network-based fuzzy classifier. IEEE Trans. Systems Man Cybernet. 25 2 (1995) 353-361
26. Zolghadri M.J., and Mansoori E.G. Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis. Inform. Sci. 177 11 (2007) 2296-2307