Fuzzy Multi-output Transfer Learning for Regression

Che, X; Zuo, H; Lu, J; Chen, D

Fuzzy Multi-output Transfer Learning for Regression

Che, X Zuo, H

Lu, J

Chen, D

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2022, 30, (7), pp. 2438-2451
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Che, X
dc.contributor.author	Zuo, H https://orcid.org/0000-0002-9122-0775
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Chen, D
dc.date.accessioned	2023-03-23T05:56:59Z
dc.date.available	2023-03-23T05:56:59Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2022, 30, (7), pp. 2438-2451
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/168229
dc.description.abstract	Multi-output regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multi-output data and the divergence of data are other factors that impede the development of multi-output regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowl- edge from a similar domain, provide a solution to the above issues. In this paper, a novel fuzzy transfer learning method is proposed to tackle the multi-output regression problems in ho- mogeneous and heterogeneous scenarios. By considering output- input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/FL190100149
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2021.3083956
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Fuzzy Multi-output Transfer Learning for Regression
dc.type	Journal Article
utslib.citation.volume	30
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-23T05:56:57Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	7

Abstract:

Multi-output regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multi-output data and the divergence of data are other factors that impede the development of multi-output regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowl- edge from a similar domain, provide a solution to the above issues. In this paper, a novel fuzzy transfer learning method is proposed to tackle the multi-output regression problems in ho- mogeneous and heterogeneous scenarios. By considering output- input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.

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http://hdl.handle.net/10453/168229