Combining interpretable fuzzy rule-based classifiers via multi-objective hierarchical evolutionary algorithm

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2011, Pages 1771-1776

  Cao, Jingjing ^a   Wang, Hanli ^b   Kwong, Sam ^a   Li, Ke ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b TONGJI UNIVERSITY   (China)

Author keywords

Ensemble diversity; Ensemble pruning; Fuzzy rule based systems (FRBCs); Interpretability; Multi objective evolutionary algorithm (MOEAs)

Indexed keywords

ENSEMBLE DIVERSITY; ENSEMBLE PRUNING; FUZZY RULE-BASED SYSTEMS; INTERPRETABILITY; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS;

CYBERNETICS; FUZZY RULES; FUZZY SYSTEMS; GENES;

EVOLUTIONARY ALGORITHMS;

EID: 83755186783     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2011.6083928     Document Type: Conference Paper

References (23)

1. L. Breiman. Bagging predictors. Machine learning, 24(2):123-140, 1996. (Pubitemid 126724382)
2. G. Castellano, A.M. Fanelli, and T. Roselli. A GA-based approach to optimize fuzzy models learned from data. 2002.
3. O. Cordón, F. Herrera, F. Hoffmann, L. Magdalena, O. Cordon, F. Herrera, and F. Hoffmann. Genetic Juzzy systems. World Scientific, 2001.
4. L.J. Eshelman. Real-coded genetic algorithms and interval-schemata. Foundations of genetic algorithms, 2:187-202, 1993.
5. Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119-139, 1997.
6. M.J. Gacto, R. Alcalá, and F. Herrera. Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems. Fuzzy Systems, IEEE Transactions on, 18(3):515-531, 2010.
7. F. Herrera, M. Lozano, and J.L. Verdegay. Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review, 12:265-319, 1998. (Pubitemid 128514089)
8. H. Ishibuchi, T. Nakashima, and T. Kuroda. A hybrid fuzzy GBML algorithm for designing compact fuzzy rule-based classification systems. In Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on, volume 2, pages 706-711. IEEE, 2000.
9. H. Ishibuchi and Y. Nojima. Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers. International Journal of Hybrid Intelligent Systems, 3(3):129-145, 2006.
10. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka. Selecting fuzzy if-then rules for classification problems using genetic algorithms. Fuzzy Systems, IEEE Transactions on, 3(3):260-270, 1995.
11. L.I. Kuncheva and C.J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2):181-207, 2003.
12. Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, and Alberto Suárez. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Pattern Anal. Mach. Intell., 31(2):245-259, 2009.
13. Corrado Mencar and Anna Maria Fanelli. Interpretability constraints for fuzzy information granulation. Inf. Sci., 178(24):4585-4618, 2008.
14. Y. Nojima and H. Ishibuchi. Effects of diversity measures on the design of ensemble classifiers by multiobjective genetic fuzzy rule selection with a multi-classifier coding scheme. Hybrid Artificial Intelligence Systems, pages 755-763, 2008.
15. H. Roubos and M. Setnes. Compact and transparent fuzzy models and classifiers through iterative complexity reduction. Fuzzy Systems, IEEE Transactions on, 9(4):516-524, 2001. (Pubitemid 32933039)
16. R.E. Schapire. The strength of weak learnability. Machine learning, 5(2): 197-227, 1990.
17. M. Setnes, R. Babuska, U. Kaymak, and H.R. van Nauta Lemke. Similarity measures in fuzzy rule base simplification. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 28(3):376-386, 1998. (Pubitemid 128752590)
18. M. Setnes and H. Roubos. Ga-fuzzy modeling and classification: complexity and performance. Fuzzy Systems, IEEE Transactions on, 8(5):509-522, 2000.
19. H. Wang, S. Kwong, Y. Jin, and C.H. Tsang. Agent based multi-objective approach to generating interpretable fuzzy systems. Multi-Objective Machine Learning, pages 339-364, 2006.
20. H. Wang, S. Kwong, Y. Jin, W. Wei, and K.F. Man. Agent-based evolutionary approach for interpretable rule-based knowledge extraction. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 35(2):143-155, 2005. (Pubitemid 40654428)
21. H. Wang, S. Kwong, Y. Jin, W. Wei, and KF Man. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy sets and systems, 149(1):149-186, 2005.
22. Y. Zhang, X. Wu, Z. Xing, and W.L. Hu. On generating interpretable and precise fuzzy systems based on pareto multi-objective cooperative co-evolutionary algorithm. Applied Soft Computing, 11(1):1284-1294, 2011.
23. Shang-Ming Zhou and John Q. Gan. Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets and Systems, 159(23):3091-3131, 2008.