Performance evaluation of evolutionary multiobjective approaches to the design of fuzzy rule-based ensemble classifiers

Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems

Volumn 2005, Issue , 2005, Pages 271-276

  Ishibuchi, Hisao ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY RULE SELECTION; GENERALIZATION ABILITY; RULE-BASED CLASSIFIERS;

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; FUZZY SETS; INTEROPERABILITY; LOGIC DESIGN;

EVOLUTIONARY ALGORITHMS;

EID: 33846954775     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICHIS.2005.88     Document Type: Conference Paper

References (22)

1. K. Deb, Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, Chichester, 2001.
2. C. A. C. Coello, D. A. van Veldhuizen, and G. B. Lamont, Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Publishers, Boston, 2002.
3. L. Breiman, "Bagging predictors," Machine Learning, vol.24, no.2, pp.123-140, 1996.
4. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol.55, no.1, pp. 119-139, 1997.
5. H. A. Abbass, "Pareto neuro-evolution: Constructing ensemble of neural networks using multi-objective optimization," Proc. of Congress on Evolutionary Computation - CEC 2003, pp. 2074-2080, 2003.
6. A. Chandra and X. Yao, "DIVACE: Diverse and accurate ensemble learning algorithm," Lecture Notes in Computer Science 3177: Intelligent Data Engineering and Automated Learning - IDEAL 2004, Springer, Berlin, pp.619-625, 2004.
7. A. Chandra and X. Yao, "Evolutionary framework for the construction of diverse hybrid ensemble," Proc. of the 13th European Symposium on Artificial Neural Networks - ESANN 2005, pp. 253-258, 2005.
8. Y. Jin, T. Okabe, and B. Sendhoff, "Neural network regularization and ensembling using multi-objective evolutionary algorithms," Proc. of Congress on Evolutionary Computation - CEC 2004, pp. 1-8, 2004.
9. Y. Jin, T. Okabe, and B. Sendhoff, "Evolutionary multi-objective optimization approach to constructing neural network ensembles for regression," In: C. A. C. Coello, G. B. Lamont (eds) Applications of Multi-Objective Evolutionary Algorithms, World Scientific, Singapore, pp.653-673, 2004.
10. L. S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Y. Suen, "Feature selection for ensembles: A hierarchical multi-objective genetic algorithm approach," Proc. of 7th International Conference on Document Analysis and Recognition - ICDAR 2003, pp.676-680, 2003.
11. L. S. Oliveira, M. Morita, R. Sabourin, and F. Bortolozzi, "Multi-objective genetic algorithms to create ensemble of classifiers," Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, Springer, Berlin, pp.592-606, 2005.
12. H. Ishibuchi and T. Yamamoto, "Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers," Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation - GECCO 2003, Springer, Berlin, pp. 1077-1088, 2003.
13. H. Ishibuchi, T. Murata, and I. B. Turksen, "Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems," Fuzzy Sets and Systems, vol.89, no.2, pp. 135-150, 1997.
14. H. Ishibuchi, T. Nakashima, and T. Murata, "Three-objective genetics-based machine learning for linguistic rule extraction," Information Sciences, vol.136, no.1-4, pp.109-133, 2001.
15. H. Ishibuchi and T. Yamamoto, "Effects of three-objective genetic rule selection on the generalization ability of fuzzy rule-based systems," Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization - EMO 2003, Springer, Berlin, pp.608-622, 2003.
16. H. Ishibuchi and T. Yamamoto, "Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining," Fuzzy Sets and Systems, vol.141, no.1, pp.59-88, 2004.
17. H. Ishibuchi and S. Namba, "Evolutionary multiobjective knowledge extraction for high-dimensional pattern classification problems," Lecture Notes in Computer Science 3242: Parallel Problem Solving from Nature - PPSN VIII, Springer, Berlin, pp.1123-1132, 2003.
18. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. on Evolutionary Computation, vol.6, no.2, pp. 182-197, 2002.
19. H. Ishibuchi, T. Nakashima, and M. Nii, Classification and modeling with linguistic information granules: Advanced approaches to linguistic data mining, Springer, Berlin, 2004.
20. H. Ishibuchi and T. Yamamoto, "Rule weight specification in fuzzy rule-based classification systems, IEEE Trans, on Fuzzy Systems (in press).
21. H. Ishibuchi and T. Yamamoto, "Comparison of heuristic criteria for fuzzy rule selection in classification problems," Fuzzy Optimization and Decision Making, vol.3, no.2, pp.119-139, 2004.
22. A. Gonzalez and R. Perez, "SLAVE: A genetic learning system based on an iterative approach," IEEE Trans, on Fuzzy Systems, vol.7, no.2, pp.176-191, 1999.