Interpretability indices for hierarchical fuzzy systems

Razak, T.R., Garibaldi, Jonathan M., Wagner, Christian, Pourabdollah, Amir and Soria, Daniele (2017) Interpretability indices for hierarchical fuzzy systems. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), 9-12 July 2017, Naples, Italy.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in Proceedings of the IEEE International Fuzzy Systems Conference. IEEE, 2017. ISBN: 9781509060344. DOI: 10.1109/FUZZ-IEEE.2017.8015616
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Eprints, Support
Date Deposited: 26 Apr 2017 08:13
Last Modified: 04 May 2020 19:02

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