Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks

Ma, G; Lu, J; Zhang, G

Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks

Ma, G Lu, J

Zhang, G

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2024, 32, (5), pp. 3094-3106
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Ma, G
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date.accessioned	2024-08-07T04:53:27Z
dc.date.available	2024-08-07T04:53:27Z
dc.date.issued	2024-05-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2024, 32, (5), pp. 3094-3106
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/180244
dc.description.abstract	Multisource domain adaptation (MSDA) refers to the task of adapting a model from multiple source domains to a target domain that shares a different distribution with all source domains. However, most of the existing MSDA works focus on crisp-valued data, while such data may not be available in some real-world scenarios. For example, data extracted by many measuring devices are not exact numbers but rather intervals. In this article, a highly challenging problem called MSDA with interval-valued target data is presented. The objective is to learn a new model for interval-valued target data by leveraging knowledge from source models trained on multiple crisp-valued source data. First, a theoretical analysis is given to inform the appropriate combination of multisource models. Then, we propose a new neural network model based on a fuzzy transformation function and fuzzy distances to address the proposed problem. The fuzzy transformation function is applied to extract valuable crisp-valued information from interval-valued target data, while fuzzy distances are designed to guide the fusion of multiple source models. Experiments on both synthetic and real-world datasets verify the superiority of our proposed MSDA method for the classification task. Furthermore, the results of the ablation study and parameter sensitivity analysis illustrate the rationality of the proposed fuzzy-distance-based model.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/FL190100149
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2024.3367456
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4901 Applied mathematics
dc.title	Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic 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
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	in_progress	*
dc.date.updated	2024-08-07T04:53:25Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	5

Abstract:

Multisource domain adaptation (MSDA) refers to the task of adapting a model from multiple source domains to a target domain that shares a different distribution with all source domains. However, most of the existing MSDA works focus on crisp-valued data, while such data may not be available in some real-world scenarios. For example, data extracted by many measuring devices are not exact numbers but rather intervals. In this article, a highly challenging problem called MSDA with interval-valued target data is presented. The objective is to learn a new model for interval-valued target data by leveraging knowledge from source models trained on multiple crisp-valued source data. First, a theoretical analysis is given to inform the appropriate combination of multisource models. Then, we propose a new neural network model based on a fuzzy transformation function and fuzzy distances to address the proposed problem. The fuzzy transformation function is applied to extract valuable crisp-valued information from interval-valued target data, while fuzzy distances are designed to guide the fusion of multiple source models. Experiments on both synthetic and real-world datasets verify the superiority of our proposed MSDA method for the classification task. Furthermore, the results of the ablation study and parameter sensitivity analysis illustrate the rationality of the proposed fuzzy-distance-based model.

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http://hdl.handle.net/10453/180244