Interpretability indices for hierarchical fuzzy systems

T. R. Razak, J. M. Garibaldi, C. Wagner, A. Pourabdollah, D. Soria

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve interpretability of fuzzy logic systems (FLSs). In recent years, a variety of indices have been proposed to measure the interpretability of FLSs such as the Nauck index and Fuzzy index. However, interpretability indices associated with HFSs have not so far been discussed. The structure of HFSs, with multiple layers, subsystems, and varied topologies, is the main challenge in constructing interpretability indices for HFSs. Thus, the comparison of interpretability between FLSs and HFSs - even at the index level - is still subject to open discussion. This paper begins to address these challenges by introducing extensions to the FLS Nauck and Fuzzy interpretability indices for HFSs. Using the proposed indices, we explore the concept of interpretability in relation to the different structures in FLSs and HFSs. Initial experiments on benchmark datasets show that based on the proposed indices, HFSs with equivalent function to FLSs produce higher indices, i.e. are more interpretable than their corresponding FLSs.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060344
Publication statusPublished - 23 Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
Duration: 9 Jul 201712 Jul 2017


Conference2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017

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