Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

International Journal of Approximate Reasoning

Volumn 44, Issue 1, 2007, Pages 4-31

  Ishibuchi, Hisao ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

Classification; Fuzzy data mining; Fuzzy systems; Genetic algorithms; Genetics based machine learning; Multiobjective optimization

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; DATA MINING; GENETIC ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION;

FUZZY DATA MINING; FUZZY SYSTEMS; GENETICS-BASED MACHINE LEARNING; MULTIOBJECTIVE OPTIMIZATION;

FUZZY SETS;

EID: 33751186914     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2006.01.004     Document Type: Article

References (33)

1. Cordon O., Herrera F., Hoffman F., and Magdalena L. Genetic Fuzzy Systems (2001), World Scientific, Singapore
2. 3 fuzzy rule systems from data using evolution strategies. IEEE Trans. Systems Man Cybernetics: Part B 29 6 (1999) 829-845
3. Jin Y. Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 8 2 (2000) 212-221
4. Setnes M., and Roubos H. GA-based modeling and classification: complexity and performance. IEEE Trans. Fuzzy Syst. 8 5 (2000) 509-522
5. In: Casillas J., Cordon O., Herrera F., and Magdalena L. (Eds). Interpretability Issues in Fuzzy Modeling (2003), Springer, Berlin
6. In: Casillas J., Cordon O., Herrera F., and Magdalena L. (Eds). Accuracy Improvements in Linguistic Fuzzy Modeling (2003), Springer, Berlin
7. F. Jimenez, A.F. Gomez-Skarmeta, G. Sanchez, H. Roubos, R. Babuska, Accurate, transparent and compact fuzzy models for function approximation and dynamic modeling through multi-objective evolutionary optimization, in: Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization, 2001, Zurich, pp. 653-667.
8. Wang H., Kwong S., Jin Y., Wei W., and Man K.F. Agent-based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Trans. Syst. Man Cybernetics: Part C 35 2 (2005) 143-155
9. Wang H., Kwong S., Jin Y., Wei W., and Man K.F. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Systems 149 1 (2005) 149-186
10. Y. Jin, T. Okabe, B. Sendhoff, Neural network regularization and ensembling using multi-objective evolutionary algorithms, in: Proceedings of 2004 Congress on Evolutionary Computation, 2004, Portland, pp. 1-8.
11. L.S. Oliveira, M. Morita, R. Sabourin, F. Bortolozzi, Multi-objective genetic algorithms to create ensemble of classifiers, in: Proceedings of the 3rd International Conference on Evolutionary Multi-Criterion Optimization, 2005, Guanajuato, pp. 592-606.
12. Ishibuchi H., Nozaki K., Yamamoto N., and Tanaka H. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets Systems 65 2-3 (1994) 237-253
13. Ishibuchi H., Nozaki K., Yamamoto N., and Tanaka H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Syst. 3 3 (1995) 260-270
14. Ishibuchi H., Murata T., and Turksen I.B. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Systems 89 2 (1997) 135-150
15. 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
16. Ishibuchi H., and Yamamoto T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Systems 141 1 (2004) 59-88
17. Ishibuchi H., Nakashima T., and Murata T. Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. Syst. Man Cybernetics: Part B 29 5 (1999) 601-618
18. Ishibuchi H., and Nakashima T. Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. Indus. Electron. 46 6 (1999) 157-168
19. Ishibuchi H., Yamamoto T., and Nakashima T. Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans. Syst. Man Cybernetics: Part B 35 2 (2005) 359-365
20. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 2 (2002) 182-197
21. Deb K. Multi-objective Optimization using Evolutionary Algorithms (2001), John Wiley & Sons, Chichester
22. Coello C.A.C., van Veldhuizen D.A., and Lamont G.B. Evolutionary Algorithms for Solving Multi-objective Problems (2002), Kluwer Academic Publishers, Boston
23. Coello C.A.C. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge Inform Syst 1 3 (1999) 269-308
24. Van Veldhuizen D.A., and Lamont G.B. Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8 2 (2000) 125-147
25. Ishibuchi H., Nakashima T., and Nii M. Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (2004), Springer, Berlin
26. Ishibuchi H., and Nakashima T. Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 9 4 (2001) 506-515
27. Ishibuchi H., and Yamamoto T. Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13 4 (2005) 428-435
28. Ishibuchi H., Nakashima T., and Morisawa T. Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Systems 103 2 (1999) 223-238
29. Cordon O., del Jesus M.J., and Herrera F. A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reasoning 20 1 (1999) 21-45
30. Elomaa T., and Rousu J. General and efficient multisplitting of numerical attributes. Mach Learning 36 3 (1999) 201-244
31. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufman, San Mateo
32. Quinlan J.R. Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4 (1996) 77-90
33. H. Ishibuchi, K. Narukawa, Comparison of evolutionary multiobjective optimization with reference solution-based single-objective approach, in: Proceedings of 2005 Genetic and Evolutionary Computation Conference, Washington, DC, 2005, pp. 787-794.