Multiple-source Domain Adaptation in Rule-based Neural Network

Zuo, H; Lu, J; Zhang, G

Multiple-source Domain Adaptation in Rule-based Neural Network

Zuo, H

Lu, J

Zhang, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-6
Issue Date:: 2020-07-01

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Field	Value	Language
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	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date	2020-07-19
dc.date.accessioned	2021-03-29T04:23:03Z
dc.date.available	2021-03-29T04:23:03Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-6
dc.identifier.isbn	9781728169262
dc.identifier.uri	http://hdl.handle.net/10453/147612
dc.description.abstract	© 2020 IEEE. Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain). Although many methods have been developed in domain adaptation, one issue hasn't been solved: how to implement knowledge transfer when more than one source domain is available. In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules. The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP170101632
dc.relation	http://purl.org/au-research/grants/arc/FL190100149
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN48605.2020.9206761
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Multiple-source Domain Adaptation in Rule-based Neural Network
dc.type	Conference Proceeding
utslib.citation.volume	00
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	open_access	*
utslib.copyright.embargo	2022-09-30T00:00:00+1000Z
dc.date.updated	2021-03-29T04:23:02Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain). Although many methods have been developed in domain adaptation, one issue hasn't been solved: how to implement knowledge transfer when more than one source domain is available. In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules. The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.

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http://hdl.handle.net/10453/147612