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Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data

Shang-ming Zhou Orcid Logo

Fuzzy Sets and Systems, Volume: 159, Issue: 23, Pages: 3091 - 3131

Swansea University Author: Shang-ming Zhou Orcid Logo

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DOI (Published version): 10.1016/j.fss.2008.05.016

Abstract

This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose f...

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Published in: Fuzzy Sets and Systems
ISSN: 0165-0114
Published: 2008
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URI: https://cronfa.swan.ac.uk/Record/cronfa13939
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spelling 2011-10-01T00:00:00.0000000 v2 13939 2013-01-21 Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2013-01-21 BMS This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested. Journal Article Fuzzy Sets and Systems 159 23 3091 3131 0165-0114 Data-driven fuzzy systems; Interpretable; Fuzzy models; Interpretability; Transparency; Criteria; Parsimony; Distinguishability; Low-level interpretability; High-level interpretability. 31 12 2008 2008-12-31 10.1016/j.fss.2008.05.016 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2011-10-01T00:00:00.0000000 2013-01-21T11:16:17.9419983 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-ming Zhou 0000-0002-0719-9353 1
title Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
spellingShingle Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
Shang-ming Zhou
title_short Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
title_full Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
title_fullStr Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
title_full_unstemmed Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
title_sort Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
author Shang-ming Zhou
author2 Shang-ming Zhou
format Journal article
container_title Fuzzy Sets and Systems
container_volume 159
container_issue 23
container_start_page 3091
publishDate 2008
institution Swansea University
issn 0165-0114
doi_str_mv 10.1016/j.fss.2008.05.016
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 0
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description This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested.
published_date 2008-12-31T03:15:57Z
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score 11.037581

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