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

Abstract

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.

Citation

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. https://doi.org/10.1109/tfuzz.2020.2969901

• 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
• DOI: https://doi.org/10.1109/tfuzz.2020.2969901
• Keywords: Control and Systems Engineering; Computational Theory and Mathematics; Applied Mathematics; Artificial Intelligence
• Public URL: https://nottingham-repository.worktribe.com/output/4115157
• Publisher URL: https://ieeexplore.ieee.org/document/8972601
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.