Fuzzy systems are universally acknowledged as valuable tools to model complex phenomena while preserving a readable form of knowledge representation. The resort to natural language for expressing the terms involved in fuzzy rules, in fact, is a key factor to conjugate mathematical formalism and logical inference with human-centered interpretability. That makes fuzzy systems specifically suitable in every real-world context where people are in charge of crucial decisions. This is because the self-explanatory nature of fuzzy rules profitably supports expert assessments. Additionally, as far as interpretability is investigated, it appears that (a) the simple adoption of fuzzy sets in modeling is not enough to ensure interpretability; (b) fuzzy knowledge representation must confront the problem of preserving the overall system accuracy, thus yielding a trade-off which is frequently debated. Such issues have attracted a growing interest in the research community and became to assume a central role in the current literature panorama of computational intelligence. This chapter gives an overview of the topics related to fuzzy system interpretability, facing the ambitious goal of proposing some answers to a number of open challenging questions: What is interpretability? Why interpretability is worth considering? How to ensure interpretability, and how to assess (quantify) it? Finally, how to design interpretable fuzzy models? The objective of this chapter is to provide some answers for the questions posed above. Section 14.1 deals with the challenging task of setting a proper definition of interpretability. Section 14.2 introduces the main constraints and criteria that can be adopted to ensure interpretability when designing interpretable fuzzy systems. Section 14.3 gives a brief overview of the soundest indexes for assessing interpretability. Section 14.4 presents the most popular approaches for designing fuzzy systems endowed with a good interpretability-accuracy trade-off. Section 14.5 enumerates some application fields where interpretability is a main concern. Section 14.6 sketches a number of challenging tasks which should be addressed in the near future. Finally, some conclusions are drawn in Sect. 14.7.

Interpretability of Fuzzy Systems: Current Research Trends and Prospects

CASTIELLO, CIRO;MENCAR, CORRADO
2015

Abstract

Fuzzy systems are universally acknowledged as valuable tools to model complex phenomena while preserving a readable form of knowledge representation. The resort to natural language for expressing the terms involved in fuzzy rules, in fact, is a key factor to conjugate mathematical formalism and logical inference with human-centered interpretability. That makes fuzzy systems specifically suitable in every real-world context where people are in charge of crucial decisions. This is because the self-explanatory nature of fuzzy rules profitably supports expert assessments. Additionally, as far as interpretability is investigated, it appears that (a) the simple adoption of fuzzy sets in modeling is not enough to ensure interpretability; (b) fuzzy knowledge representation must confront the problem of preserving the overall system accuracy, thus yielding a trade-off which is frequently debated. Such issues have attracted a growing interest in the research community and became to assume a central role in the current literature panorama of computational intelligence. This chapter gives an overview of the topics related to fuzzy system interpretability, facing the ambitious goal of proposing some answers to a number of open challenging questions: What is interpretability? Why interpretability is worth considering? How to ensure interpretability, and how to assess (quantify) it? Finally, how to design interpretable fuzzy models? The objective of this chapter is to provide some answers for the questions posed above. Section 14.1 deals with the challenging task of setting a proper definition of interpretability. Section 14.2 introduces the main constraints and criteria that can be adopted to ensure interpretability when designing interpretable fuzzy systems. Section 14.3 gives a brief overview of the soundest indexes for assessing interpretability. Section 14.4 presents the most popular approaches for designing fuzzy systems endowed with a good interpretability-accuracy trade-off. Section 14.5 enumerates some application fields where interpretability is a main concern. Section 14.6 sketches a number of challenging tasks which should be addressed in the near future. Finally, some conclusions are drawn in Sect. 14.7.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/37851
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