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Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach

Soria, Daniele; Razak, Tajul Rosli; Garibaldi, Jonathan M.; Pourabdollah, Amir; Wagner, Christian


Daniele Soria

Tajul Rosli Razak

Amir Pourabdollah


Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed assessment of how HFSs should be designed to enhance interpretability?". The challenges of measuring the interpretability of HFSs include issues such as their topological structure, the number of layers, the meaning of intermediate variables, and so on. In this paper, an initial framework to measure the interpretability of HFSs is proposed, combined with a participatory user design process to create a specific instance of the framework for an application context. This approach enables the subjective views of a range of practitioners, experts in the design and creation of FLSs, to be taken into account in shaping the design of a generic framework for measuring interpretability in HFSs. This design process and framework are demonstrated through two classification application examples, showing the ability of the resulting index to appropriately capture interpretability as perceived by system design experts.


Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2020). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 1-1.

Journal Article Type Article
Acceptance Date Jan 28, 2020
Online Publication Date Jan 28, 2020
Publication Date Jan 28, 2020
Deposit Date Mar 9, 2020
Publicly Available Date Mar 9, 2020
Journal IEEE Transactions on Fuzzy Systems
Print ISSN 1063-6706
Electronic ISSN 1941-0034
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 1-1
Keywords Control and Systems Engineering; Computational Theory and Mathematics; Applied Mathematics; Artificial Intelligence
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