A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure

Bai, K; Zhang, W; Wen, S; Zhao, C; Meng, W; Zeng, Y; Jia, D

A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure

Bai, K Zhang, W Wen, S

Zhao, C Meng, W Zeng, Y Jia, D

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2024, 660, pp. 120133
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Bai, K
dc.contributor.author	Zhang, W
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Zhao, C
dc.contributor.author	Meng, W
dc.contributor.author	Zeng, Y
dc.contributor.author	Jia, D
dc.date.accessioned	2024-03-07T04:29:37Z
dc.date.available	2024-03-07T04:29:37Z
dc.date.issued	2024-03-01
dc.identifier.citation	Information Sciences, 2024, 660, pp. 120133
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/176262
dc.description.abstract	Interval type-2 fuzzy neural networks (IT2FNNs) have gained sustainable attention and wide applications because of their power of adaptive fuzzy modeling. Although the existing models accurately simulate the relationships between inputs and outputs by various adaptive learning algorithms, the parameters and fuzzy rules characterizing the model behaviors no longer have explicit interpretations. To achieve good interpretability and generalization performance, this paper proposes a novel data-knowledge-driven IT2FNN (DKIT2FNN). Unlike accuracy-oriented model fitting, DKIT2FNN pioneers the use of famous association rule mining techniques to automatically explore the interpretable rule knowledge, which intuitively presents conditional relation patterns among features. Using the obtained knowledge, the DKIT2FNN model is accomplished in a single-hidden layer network framework, in which the antecedent structure of each fuzzy rule is determined by the relation patterns provided by rule knowledge. In addition, an adversarial incremental learning algorithm is proposed for the self-organizing modeling of DKIT2FNN. Besides automatically assembling a compact rule base, it integrates the label adversarial training to reinforce the model's capability against label noise. We validate the DKIT2FNN on classification benchmark datasets with label noise and compare it with state-of-the-art non-fuzzy and neuro-fuzzy methods. The results demonstrate that the proposed DKIT2FNN achieves robust classification performance and good interpretability.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2024.120133
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure
dc.type	Journal Article
utslib.citation.volume	660
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-07T04:29:36Z
pubs.publication-status	Accepted
pubs.volume	660

Abstract:

Interval type-2 fuzzy neural networks (IT2FNNs) have gained sustainable attention and wide applications because of their power of adaptive fuzzy modeling. Although the existing models accurately simulate the relationships between inputs and outputs by various adaptive learning algorithms, the parameters and fuzzy rules characterizing the model behaviors no longer have explicit interpretations. To achieve good interpretability and generalization performance, this paper proposes a novel data-knowledge-driven IT2FNN (DKIT2FNN). Unlike accuracy-oriented model fitting, DKIT2FNN pioneers the use of famous association rule mining techniques to automatically explore the interpretable rule knowledge, which intuitively presents conditional relation patterns among features. Using the obtained knowledge, the DKIT2FNN model is accomplished in a single-hidden layer network framework, in which the antecedent structure of each fuzzy rule is determined by the relation patterns provided by rule knowledge. In addition, an adversarial incremental learning algorithm is proposed for the self-organizing modeling of DKIT2FNN. Besides automatically assembling a compact rule base, it integrates the label adversarial training to reinforce the model's capability against label noise. We validate the DKIT2FNN on classification benchmark datasets with label noise and compare it with state-of-the-art non-fuzzy and neuro-fuzzy methods. The results demonstrate that the proposed DKIT2FNN achieves robust classification performance and good interpretability.

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http://hdl.handle.net/10453/176262