Towards hierarchical fuzzy rule interpolation

Jin, Shangzhu; Peng, Jun

doi:10.1109/icci-cc.2015.7259396

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…Previous research has shown that HFSs have been used to improve interpretability [5], [14]- [17]. However, to the authors' knowledge, no one has investigated how interpretability can similarly be measured using indices in HFSs.…”

Section: B Hierarchical Fuzzy Systemsmentioning

confidence: 99%

“…However, key challenges remain around FLS interpretability, including the curse of dimensionality: the number of required rules commonly increases exponentially with the number of input variables [4]. This challenge is also known as rule explosion which may reduce the transparency and interpretability of FLSs [5]. One effective way to deal with this problem is through the use of a special type of FLS, namely hierarchical fuzzy systems (HFSs) [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, HFSs tend to reduce rule explosion, thus minimizing complexity, and improving model interpretability. So far, the potential of HFSs to support good interpretability in FLSs has not been explored in detail, with only a small number of works considering it [5], [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

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Interpretability indices for hierarchical fuzzy systems

Razak

Garibaldi

Wagner

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-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.

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Section: B Hierarchical Fuzzy Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpretability indices for hierarchical fuzzy systems

Razak

Garibaldi

Wagner

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…However, key challenges remain in the design of FLSs, such as the fact that the number of rules required commonly increases exponentially with the number of input variables [5]. This challenge also known as rule ex-plosion, sometimes referred to as the curse of dimensionality, can reduce the transparency and interpretability of FLSs [6].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the rules in HFSs commonly have antecedents with fewer variables than the rules in 'flat' FLSs with equivalent function, since the number of input variables of each subsystem is lower [9], [10]. HFSs can thereby address rule explosion and thus provide a potentially valuable pathway to interpretability in FLSs [6], [11]- [15], [16]. However, whilst the number of rules can be reduced, it is an open question as to how interpretability is affected when systems are hierarchical, featuring various subsystems, layers and topologies.…”

Section: Introductionmentioning

confidence: 99%

Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach

Razak

Garibaldi

Wagner

et al. 2021

IEEE Trans. Fuzzy Syst.

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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.

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