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


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

November 30, 2015
June 17, 2016
September 20, 2016
fuzzy sets, parameter learning, membership function, adaptive factors, medical diagnosis
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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