A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems

Hierarchical fuzzy systems (HFSs) are claimed to be an excellent approach to reducing the number of rules in Fuzzy logic systems (FLSs). Further, HFSs have also been shown to have the potential to reduce complexity and improve interpretability for FLSs. However, designing an interpretable HFS is a c...

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Published in:2024 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ-IEEE 2024
Main Authors: Razak, Tajul Rosli; Garibaldi, Jonathan M.; Wagner, Christian
Format: Proceedings Paper
Language:English
Published: IEEE 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001293753100095
author Razak
Tajul Rosli; Garibaldi
Jonathan M.; Wagner
Christian
spellingShingle Razak
Tajul Rosli; Garibaldi
Jonathan M.; Wagner
Christian
A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
Computer Science; Mathematics
author_facet Razak
Tajul Rosli; Garibaldi
Jonathan M.; Wagner
Christian
author_sort Razak
spelling Razak, Tajul Rosli; Garibaldi, Jonathan M.; Wagner, Christian
A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
2024 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ-IEEE 2024
English
Proceedings Paper
Hierarchical fuzzy systems (HFSs) are claimed to be an excellent approach to reducing the number of rules in Fuzzy logic systems (FLSs). Further, HFSs have also been shown to have the potential to reduce complexity and improve interpretability for FLSs. However, designing an interpretable HFS is a challenging task. This is due to the HFSs' structures, which have multiple subsystems, layers and topologies. This paper put forward an approach to present a design guidelines framework to build interpretable HFSs. The framework consists of five key guidelines for interpretable HFSs. It is demonstrated using a real-world example to design interpretable HFS for the Iris classification problem. This study contributes to providing insight into a pathway for designing interpretable HFSs, as can be used in practice.
IEEE
1544-5615

2024


10.1109/FUZZ-IEEE60900.2024.10612113
Computer Science; Mathematics

WOS:001293753100095
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001293753100095
title A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
title_short A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
title_full A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
title_fullStr A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
title_full_unstemmed A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
title_sort A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems
container_title 2024 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ-IEEE 2024
language English
format Proceedings Paper
description Hierarchical fuzzy systems (HFSs) are claimed to be an excellent approach to reducing the number of rules in Fuzzy logic systems (FLSs). Further, HFSs have also been shown to have the potential to reduce complexity and improve interpretability for FLSs. However, designing an interpretable HFS is a challenging task. This is due to the HFSs' structures, which have multiple subsystems, layers and topologies. This paper put forward an approach to present a design guidelines framework to build interpretable HFSs. The framework consists of five key guidelines for interpretable HFSs. It is demonstrated using a real-world example to design interpretable HFS for the Iris classification problem. This study contributes to providing insight into a pathway for designing interpretable HFSs, as can be used in practice.
publisher IEEE
issn 1544-5615

publishDate 2024
container_volume
container_issue
doi_str_mv 10.1109/FUZZ-IEEE60900.2024.10612113
topic Computer Science; Mathematics
topic_facet Computer Science; Mathematics
accesstype
id WOS:001293753100095
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001293753100095
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collection Web of Science (WoS)
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