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JACIII Vol.20 No.5 pp. 773-787
doi: 10.20965/jaciii.2016.p0773
(2016)

Paper:

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An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems

Yanni Wang*1, Yaping Dai*1, Yu-Wang Chen*2, and Witold Pedrycz*3,*4

*1School of Automation, Beijing Institute of Technology
Zhongguancun Street 5, Beijing, Haidian District, China

*2Alliance Manchester Business School, University of Manchester
Manchester, M15 6PB, United Kingdom

*3Department of Electrical & Computer Engineering, University of Alberta
Edmonton, Alberta T6G 2J7, Canada

*4Systems Research Institute, Polish Academy of Sciences
Newelska 6, 01-447, Warsaw, Poland

Received:
November 30, 2015
Accepted:
June 17, 2016
Published:
September 20, 2016
Creative Commons License
Keywords:
fuzzy sets, parameter learning, membership function, adaptive factors, medical diagnosis
Abstract
Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.
Cite this article as:
Y. Wang, Y. Dai, Y. Chen, and W. Pedrycz, “An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.5, pp. 773-787, 2016.
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References
  1. [1] J. Casillas, O. Cordón, F. Herrera, and L. Magdalena, “Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview,” Springer-Verlag Berlin Heidelberg, pp. 1-20, 2003.
  2. [2] S. M. Zhou and J. Q. Gan, “Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system Modeling,” Fuzzy Sets and Systems, Vol.159, pp. 3091-3131, 2008.
  3. [3] Z. Mucong, S. Zhongke, and L. Yan, “Triple I method of approximate reasoning on Atanassov’s intuitionistic fuzzy sets,” Int. J. of Approximate Reasoning, Vol.55, pp. 1369-1382, 2014.
  4. [4] C. Cornelis, G. Deschrijver, and E. E. Kerre, “Implication in intuitionistic fuzzy and interval-valued fuzzy set theory: construction, classification, application,” Int. J. of Approximate Reasoning, Vol.35, pp. 55-95, 2004.
  5. [5] O. Castillo, A. Alanis, M. Garcia, and H. Arias, “An intuitionistic fuzzy system for time series analysis in plant monitoring and diagnosis,” Applied Soft Computing, Vol.7, pp. 1227-1233, 2007.
  6. [6] H. Bustince, “Indicator of inclusion grade for interval-valued fuzzy sets. Application to approximate reasoning based on interval-valued fuzzy sets,” Int. J. of Approximate Reasoning, Vol.23, pp. 137-209, 2000.
  7. [7] R. R. Yager, “Some aspects of intuitionistic fuzzy sets,” Fuzzy Optim Decis Making, Vol.8, pp. 67-90, 2009.
  8. [8] C. Dongfeng, F. Yu, and L. Yongxue, “Threat Assessment for Air Defense Operations Based on Intuitionistic Fuzzy Logic,” Procedia Engineering, Vol.29, pp. 3302-3306, 2012.
  9. [9] Z. Xu and R. R. Yager, “Intuitionistic and interval-valued intutionistic fuzzy preference relations and their measures of similarity for the evaluation of agreement within a group,” Fuzzy Optim Decis Making, Vol.8, pp. 123-139, 2009.
  10. [10] E. Szmidt and J. Kacprzyk, “Distances between intuitionistic fuzzy sets,” Fuzzy Sets and Systems, Vol.114, pp. 505-518, 2000.
  11. [11] H. M. Nehi and H. R. Maleki, “Intuitionistic fuzzy numbers and it’s applications in fuzzy optimization problem,” Proc. of the 9th WSEAS Int. Conf. on Systems, pp.1-5, 2005.
  12. [12] J. Ye, “Expected value method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems,” Expert Systems with Applications, Vol.38, pp. 11730-11734, 2011.
  13. [13] M. Mizumoto and H. J. Zimmermann, “Comparison of fuzzy reasoning methods,” Fuzzy sets and systems, Vol.8, pp. 253-283, 1982.
  14. [14] 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 and Systems, Vol.149, No.1, pp. 149-186, 2005.
  15. [15] M. Galende, M. J. Gacto, G. Sainz, and R. Alcalá, “Comparison and design of interpretable linguistic vs. scatter FRBSs: GM3M generalization and new rule meaning index for global assessment and local pseudo-linguistic representation,” Information Sciences, Vol.282, pp. 190-213, 2014.
  16. [16] M. J. Gacto, R. Alcalá, and F. Herrera, “Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures,” Information Science, Vol.181, pp. 4340-4360, 2011.
  17. [17] Y. N. Wang, Y. P. Dai, and F. C. Meng, “Similarity measure of intuitionistic trapezoidal fuzzy numbers and its application for medical diagnosis,” The 32nd Proc. of the Chinese Control Conf. (CCC), pp. 8567-8571, 2013.
  18. [18] P. Grzegrozewski, “Ditances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric,” Fuzzy Sets and Systems, Vol.148, pp. 319-328, 2004.
  19. [19] P. Grzegrozewski, “The hamming distance between intuitionistic fuzzy sets,” Proc. of the 10th IFSA World Congress, pp. 35-38, 2003.
  20. [20] M. Setnes, R. Babuška, and H. B. Verbruggen, “Rule-Based Modeling: Precision and Transparency, IEEE transactions on systems, man, and cybernetics-part C: applications and review,” Vol.28, No.1, pp. 165-169, 1998.
  21. [21] Z. Xu and R. R. Yager, “Intuitionistic and interval-valued intutionistic fuzzy preference relations and their measures of similarity for the evaluation of agreement within a group,” Fuzzy Optim Decis Making, Vol.8, pp. 123-139, 2009.
  22. [22] O. Cordón, F. Herrera, F. A. Márquez, and A. Peregrín, “A study on the evolutionary adaptive defuzzification methods in fuzzy modeling,” Int. J. of Hybrid Intelligent Systems, Vol.1, No.1, pp. 36-48, 2004.
  23. [23] O. Castillo, E. Lizárraga, J. Soria, P. Melin, and F. Valdez, “New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system,” Information Sciences, Vol.294, pp. 203-215, 2015.
  24. [24] O. Castillo, H. Neyoy, J. Soria, P. Melin, and F. Valdez, “A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot,” Applied Soft Computing, Vol.28, pp. 150-159, 2015.
  25. [25] M. H. Khooban and T. Niknam, “A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm,” Electrical Power and Energy Systems, Vol.71, pp. 254-261, 2015.
  26. [26] P. GaneshKumar, C. Rani, D. Devaraj, and T. A. A. Victoire, “Hybrid ant bee algorithm for fuzzy expert system based sample classification,” IEEE/ACM Trans. on Computational Biology And Bioinformatics, Vol.11, No.2, pp. 347-360, 2014.
  27. [27] F. Jiménez, G. Sánchez, and J. M. Juárez, “Multi-objective evolutionary algorithms for fuzzy classification in survival prediction,” Artificial Intelligence in Medicine, Vol.60, pp. 197-219, 2014.
  28. [28] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. on Systems, Man, and Cybernetics , Vol.3, pp. 28-44, 1973.
  29. [29] J. Alcalá-Fdez, F. Herrera, F. Márquez, and A. Peregrín, “Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems,” Int. J. of Intelligent systems, Vol.22, pp. 1035-1064, 2007.
  30. [30] V. Khatibi and G. Ali Montazer, “Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition,” Artificial Intelligence in Medicine, Vol.47, pp. 43-52, 2009.
  31. [31] http://www.ics.uci.edu/˜mlearn/MLRepository.html, UCI repository of machine learning databases, 1998.
  32. [32] S. Destercke, S. Guillaume, and B. Charnomordic, “Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem,” Fuzzy Sets and Systems, Vol.158, No.18, pp. 2078-2094, 2007.
  33. [33] W. Duch, R. Setiono, and J. M. .Zurada, “Computational intelligence methods for rule-based data understanding,” Proc. of the IEEE, Vol.92, No.5, pp. 771-805, 2004.

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