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Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems

  • Chapter
Multi-Objective Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 16))

Abstract

Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature.

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References

  1. L.A. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst., Man, Cybern., Vol. SMC-3, pp. 28–44, Jan. 1973

    MathSciNet  Google Scholar 

  2. G. Castellano, A.M. Fanelli, E. Gentile and T. Roselli. A GA-based approach to optimize fuzzy models learned from data, Proc. Workshop on Approx. Learning in Evol. Comput., GECCO 2002, pp. 5–8, Jul. 2002

    Google Scholar 

  3. S. Guillaume. Designing Fuzzy Inference Systems from Data: An interpretability Oriented Review, IEEE Trans. Fuzzy Syst., Vol.9, No.3, pp. 426–443, Jun. 2001

    Article  Google Scholar 

  4. F. Jiménez, A. F. Gómez-Skarmeta, H. Roubos, and R. Babuška. Accurate, Transparent, and Compact Fuzzy Models for Function Approximation and Dynamic Modeling through Multi-objective Evolutionary Optimization, EMO 2001, pp. 653–667, 2001

    Google Scholar 

  5. Y. Jin. Advanced Fuzzy Systems Design and Applications. Physica/Springer, Heidelberg, 2003

    MATH  Google Scholar 

  6. Y. Jin, W. von Seelen and B. Sendhoff. An approach to Rule-Based Knowledge Extraction, IEEE Conf. Fuzzy Syst., Vol. 2, pp. 1188–1193, May 1998

    Google Scholar 

  7. Y. Jin, W. von Seelen, and B. Sendhoff. On Generating FC3 Fuzzy Rule Systems from Data Using Evolution Strategies, IEEE Trans. Syst., Man, Cybern. B, Vol. 29, No. 6, pp.829–845, Dec. 1999

    Article  Google Scholar 

  8. Y. Jin. Fuzzy Modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Trans. Fuzzy Syst., Vol. 8, No. 2, pp. 212–221, 2000

    Article  Google Scholar 

  9. Y. Jin and B. Sendhoff. Extracting Interpretable Fuzzy Rules from RBF Networks, Neural Processing Letters, 17(2), pp. 149–164, 2003

    Article  MATH  Google Scholar 

  10. I. Rojas, H. Pomares, J. Ortega, and A. Prieto. Self-Organized Fuzzy System Generation from training Examples, IEEE Trans. Fuzzy Syst., Vol. 8, No. 1, pp. 23–36, Feb. 2000

    Article  Google Scholar 

  11. H. Roubos and M. Setnes. GA-Fuzzy Modeling and Classification: Complexity and Performance, IEEE Trans. Fuzzy Syst., Vol. 8, No. 5, pp. 509–522, Oct. 2000

    Google Scholar 

  12. H. Roubos and M. Setnes. Compact and Transparent Fuzzy Models and Classifiers Through Iterative Complexity Reduction, IEEE Trans. Fuzzy Syst., Vol. 9, No. 4, pp. 516–524, Aug. 2001

    Article  Google Scholar 

  13. L. Wang and J. Yen. Exacting Fuzzy Rules for System Modeling Using a Hybrid of Genetic Algorithms and Kalman Filter, Fuzzy Sets Syst., Vol. 101, pp. 353–362, 1999

    Article  MathSciNet  Google Scholar 

  14. J. Yen and L. Wang. Application of Statistical Information Criteria for Optimal Fuzzy Model Construction, IEEE Trans. Fuzzy Syst., Vol. 6, No. 3, pp. 362–372, 1998

    Article  Google Scholar 

  15. J. Yen and L. Wang. Simplifying Fuzzy Rule-Based Models Using Orthogonal Transformation Methods, IEEE Trans. Syst., Man, Cybern. B, Vol. 29, No. 1, pp. 13–24, 1999

    Article  Google Scholar 

  16. J. Casillas, O. Cordón, F. Herrera, and L. Magdalena (Eds.). Interpretability Issues in Fuzzy Modeling, SPRINGER, BERLIN 2003

    MATH  Google Scholar 

  17. H. Wang, S. Kwong, Y. Jin, W. Wei, and K. F. Man. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets Syst., Vol. 149, No. 1, pp. 149–186, Jan. 2005

    Article  MATH  MathSciNet  Google Scholar 

  18. H. Wang, S. Kwong, Y. Jin, W. Wei, and K. F. Man. Agent-Based Evolutionary Approach for Interpretable Rule-Based Knowledge Extraction, IEEE Trans. Syst., Man, Cybern. C, Vol. 35, No. 2, pp. 143–155, May 2005

    Article  Google Scholar 

  19. O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena. Ten years of genetic fuzzy systems: current framework and new trends, Fuzzy Sets Syst., Vol. 141, No. 1, pp. 5–31, Jan. 2004

    Article  MATH  Google Scholar 

  20. N. Xiong. Evolutionary learning of rule premises for fuzzy modeling, Int. Journal of Syst. Sci., Vol. 32, No. 9, pp. 1109–1118, 2001

    Article  MATH  Google Scholar 

  21. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka. Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms, IEEE Trans. Fuzzy Syst., Vol. 3, No. 3, pp. 260–270, Aug. 1995

    Article  Google Scholar 

  22. H. Ishibuchi, T. Nakashima, and T. Kuroda. A Hybrid Fuzzy Genetics-based Machine Learning Algorithm: Hybridization of Michigan Approach and Pittsburgh Approach, IEEE Int. Conf. Syst., Man, Cybern., pp. 296–301, Oct. 1999

    Google Scholar 

  23. H. Ishibuchi, T. Nakashima, and T. Murata. Performance Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern Classification Problems, IEEE Trans. Syst., Man, Cybern. B, Vol. 29, No. 5, pp. 601–618, Oct. 1999

    Article  Google Scholar 

  24. H. Ishibuchi, T. Nakashima, and T. Kuroda. A Hybrid Fuzzy GBML Algorithm for Designing Compact Fuzzy Rule-Based Classification Systems, 9th IEEE Int. Conf. Fuzzy Syst., pp.706–711, May 2000

    Google Scholar 

  25. H. Ishibuchi, T. Nakashima and T. Murata. Multi-objective optimization in linguistic rule extraction from numerical data, EMO 2001, pp. 588–602, 2001

    Google Scholar 

  26. H. Ishibuchi and T. Nakashima. Effect of Rule Weight in Fuzzy Rule-based Classification Systems, IEEE Trans. Fuzzy Syst., Vol. 9, No. 4, pp. 506–515, 2001

    Article  Google Scholar 

  27. H. Ishibuchi and T. Nakashima. Three-Objective Optimization in Linguistic Function Approximation, in Proc. Evol. Comput., pp. 340–347, May 2001

    Google Scholar 

  28. H. Ishibuchi and T. Yamamoto. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining, Fuzzy Sets Syst., Vol. 141, No. 1, pp. 59–88, Jan. 2004

    Article  MATH  MathSciNet  Google Scholar 

  29. H. Ishibuchi and S. Namba. Evolutionary Multiobjective Knowledge Extraction for High-Dimensional Pattern Classification Problems, PPSN VIII, LNCS 3242, pp. 1123–1132, 2004

    Google Scholar 

  30. T. Murata, S. Kawakami, H. Nozawa, M. Gen, and H. Ishibuchi. Three- Objective Genetic Algorithms for Designing Compact Fuzzy Rule-Based Systems for Pattern Classification Problems, GECCO 2001, pp. 485–492, Jul. 2001

    Google Scholar 

  31. K. F. Man, K. S. Tang and S. Kwong. Genetic Algorithms: Concepts and Applications, IEEE Trans. Ind. Electron., Vol. 43, No. 5, pp. 519–534, Oct. 1996

    Article  Google Scholar 

  32. K. S. Tang, K. F. Man, S. Kwong and Q. He. Genetic Algorithms and Their Applications, IEEE Signal Processing Mag., Vol. 13, No. 6, pp. 22–37, Nov. 1996

    Article  Google Scholar 

  33. K. S. Tang, K. F. Man, Z. F. Liu and S. Kwong. Minimal Fuzzy Memberships and Rules Using Hierarchical Genetic Algorithms, IEEE Trans. Ind. Electron., Vol. 45, No. 1, pp. 162–169, Feb. 1998

    Article  Google Scholar 

  34. M. Setnes, R. Babuška, U. Kaymak, and H. R. van Nauta Lemke, Similarity Measures in Fuzzy Rule Base Simplification, IEEE Trans. Syst., Man, Cybern. B, Vol. 28, No. 3, pp. 376–386, Jun. 1998

    Article  Google Scholar 

  35. K. P. Sycara. The Many Faces of Agents, AI Mag., Vol. 19, No. 2, pp. 11–12, 1998

    Google Scholar 

  36. A. H. Bond and L. Gasser, An analysis of problems and research in DAI, in Readings in Distributed Artificial Intelligence, A. H. Bond et al., Eds., 1988, pp. 3–35, 1988

    Google Scholar 

  37. B. Moulin and B. Chaib-Draa. An overview of distributed artificial intelligence, in Foundations of Distributed Artificial Intelligence, G. M. P. O'Hare et al., Eds., John Wiley & Sons Inc. New York, 1996, pp. 3–55

    Google Scholar 

  38. N. R. Jennings, K. Sycara, and M. Wooldridge. A roadmap of agent research and development, Autonomous Agents and Multi-Agent Systems, pp. 7–38, 1998

    Google Scholar 

  39. G. Weiss (Eds.). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, Cambridge, Massachusetts, 1999

    Google Scholar 

  40. S. F. Smith. A learning system based on genetic adaptive algorithms, Doctoral Disseration, Department of Computer Science, University of Pittsburgh, 1980

    Google Scholar 

  41. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., Vol. 6, No. 2, pp. 182–197, Apr. 2002

    Article  Google Scholar 

  42. UCI Machine Learning Repository [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html

    Google Scholar 

  43. K. Deb. Multi-Objective Optimization using Evolutionary Algorithms, John Willy & Sons, Chichester, UK, 2001, pp. 28–46

    MATH  Google Scholar 

  44. M. Russo. Genetic Fuzzy Learning, IEEE Trans. Evol. Comput., Vol. 4, No. 3, pp. 259–273, Sept. 2000

    Article  Google Scholar 

  45. N. Xiong and L. Litz. Identifying Flexible Structured Premises for Mining Concise Fuzzy Knowledge, in Interpretability Issues in Fuzzy Modeling, J. Casillas et al., Eds., 2003, pp. 54–76

    Google Scholar 

  46. M. Dash, H. Liu, and J. Yao. Dimensionality Reduction for Unsupervised Data, Proc. 9th Int. Conf. Tools Artif. Intell., pp. 532–539, Nov. 1997

    Google Scholar 

  47. X. Fu and L. Wang. Data Dimensionality Reduction With Application to Simplifying RBF Network Structure and Improving Classification Performance, IEEE Trans. Syst., Man, Cybern. B, Vol. 33, No. 3, pp. 399–409, Jun. 2003

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Hanli Wang, Sam Kwong, Yaochu Jin & Chi-Ho Tsang

Authors
  1. Hanli Wang
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  2. Sam Kwong
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  3. Yaochu Jin
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  4. Chi-Ho Tsang
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Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, Offenbach, 63073, Germany

    Yaochu Jin Dr.

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© 2006 Springer

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Wang, H., Kwong, S., Jin, Y., Tsang, CH. (2006). Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_15

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  • DOI: https://doi.org/10.1007/3-540-33019-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30676-4

  • Online ISBN: 978-3-540-33019-6

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