A visual explanation system for explaining fuzzy reasoning results by fuzzy rule-based classifiers

Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Volumn , Issue , 2008, Pages

  Ishibuchi, Hisao ^a   Kaisho, Yutaka ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; COMPUTER NETWORKS; FUZZY SETS; LEARNING SYSTEMS; ONLINE SEARCHING;

FUZZY REASONING; INPUT PATTERNS;

CLASSIFICATION (OF INFORMATION);

EID: 51149104605     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NAFIPS.2008.4531256     Document Type: Conference Paper

References (25)

1. S. Guillaume, "Designing fuzzy inference systems from data: An interpretability-oriented review," IEEE Trans. on Fuzzy Systems 9 (3) 426-443, June 2001.
2. R. Mikut, J. Jakel, and L. Groll, "Interpretability issues in data-based learning of fuzzy systems," Fuzzy Sets and Systems 150 (2) 179-197, March 2005.
3. J. Casillas, O. Cordon, F. Herrera, and L. Magdalena (eds.), Interpretability Issues in Fuzzy Modeling, Springer, Berlin, 2003.
4. J. Casillas, O. Cordon, F. Herrera, and L. Magdalena (eds.), Accuracy Improvements in Linguistic Fuzzy Modeling, Springer, Berlin, 2003.
5. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka, "Selecting fuzzy if-then rules for classification problems using genetic algorithms," IEEE Trans. on Fuzzy Systems 3 (3) 260-270, August 1995.
6. J. V. de Oliveira, "Semantic constraints for membership function optimization," IEEE Trans. on Systems, Man, and Cybernetics: Part A - Systems and Humans 29 (1) 128-138, January 1999.
7. D. Nauck and R. Kruse, "Obtaining interpretable fuzzy classification rules from medical data," Artificial Intelligence in Medicine 16 (2) 149-169, June 1999.
8. Y. Jin, "Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement," IEEE Trans. on Fuzzy Systems 8 (2) 212-221, April 2000.
9. M. Setnes and H. Roubos, "GA-based modeling and classification: Complexity and performance," IEEE Trans. on Fuzzy Systems 8 (5) 509-522, October 2000.
10. 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 89 (2) 135-150, July 1997.
11. H. Ishibuchi, T. Nakashima, and T. Murata, "Three-objective genetics-based machine learning for linguistic rule extraction," Information Sciences 136 (1-4) 109-133, August 2001.
12. 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 141 (1) 59-88, January 2004.
13. H. Ishibuchi and Y. Nojima, "Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning," International Journal of Approximate Reasoning 44 (1) 4-31, January 2007.
14. H. Ishibuchi, Y. Kaisho, and Y. Nojima, "Designing fuzzy rule-based classifiers that can visually explain their classification results to human users," Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, pp. 5-10, March 4-7, 2008.
15. M. R. Berthold and L. O. Hall, "Visualizing fuzzy points in parallel coordinates," IEEE Trans. on Fuzzy Systems 11 (3) 369-374, June 2003.
16. F. Rehm, R. Klawonn, and R. Kruse, "Visualization of single clusters," Lecture Notes in Computer Science 4029, pp. 663-771, Springer, 2006.
17. H. Ishibuchi, T. Nakashima, and T. Murata, "Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems," IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics 29 (5) 601-618, October 1999.
18. O. Cordon, M. J. del Jesus, and F. Herrera, "A proposal on reasoning methods in fuzzy rule-based classification systems," International Journal of Approximate Reasoning 20 (1) 21-45, January 1999.
19. H. Ishibuchi, T. Nakashima, and T. Morisawa, "Voting in fuzzy rule-based systems for pattern classification problems," Fuzzy Sets and Systems 103 (2) 223-238, April 1999.
20. L. I. Kuncheva, Fuzzy Classifier Design, Physica-Verlag, Heidelberg, 2000.
21. H. Ishibuchi, T. Nakashima, and M. Nii, Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining, Springer, Berlin, 2004.
22. H. Ishibuchi and T. Yamamoto, "Rule weight specification in fuzzy rule-based classification systems," IEEE Trans. on Fuzzy Systems 13 (4) 428-435, August 2005.
23. H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rule-based classification systems," IEEE Trans. on Fuzzy Systems 9 (4) 506-515, August 2001.
24. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, "Fast discovery of association rules," in U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining, AAAI, Menlo Park, pp. 307-328, 1996.
25. P. Pulkkinen and H. Koivisto, "Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms," International Journal of Approximate Reasoning (in press, available online).