Truncated gradient confidence-weighted based online learning for imbalance streaming data

Hu, J; Yan, C; Liu, X; Zhang, J; Peng, D; Yang, Y

Truncated gradient confidence-weighted based online learning for imbalance streaming data

Hu, J Yan, C Liu, X Zhang, J Peng, D Yang, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2019, 2019-July, pp. 133-138
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Hu, J
dc.contributor.author	Yan, C
dc.contributor.author	Liu, X
dc.contributor.author	Zhang, J
dc.contributor.author	Peng, D
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546
dc.date	2019-07-08
dc.date.accessioned	2020-06-09T08:42:38Z
dc.date.available	2020-06-09T08:42:38Z
dc.date.issued	2019-07-01
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2019, 2019-July, pp. 133-138
dc.identifier.isbn	9781538695524
dc.identifier.issn	1945-7871
dc.identifier.issn	1945-788X
dc.identifier.uri	http://hdl.handle.net/10453/141211
dc.description.abstract	© 2019 IEEE. Online learning for imbalanced streaming data is an important and challenging problem for many classification tasks in the machine learning research field. Traditional online learning algorithms are mainly focused on classification tasks with balanced data, and with little consideration about the characteristics of imbalanced streaming data. In this paper, we propose a novel online learning algorithm called Truncated Gradient Confidence-Weighted (TGCW), which integrate the truncated gradient algorithm with the confidence weighted algorithm together to improve the feature selection ability while reducing the dimensions of imbalanced streaming data effectively. We study a number of classification tasks with various imbalance data ratio including the pedestrian detection application and compare the performance of the TGCW algorithm with traditional online learning algorithms, and empirical results show that the TGCW algorithm can achieve better performance consistently than other baseline approaches.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo
dc.relation.ispartof	IEEE International Conference on Multimedia and Expo
dc.relation.isbasedon	10.1109/ICME.2019.00031
dc.rights	© 2019 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/restrictedAccess
dc.title	Truncated gradient confidence-weighted based online learning for imbalance streaming data
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
utslib.location.activity	Shanghai, China
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-09T08:42:35Z
pubs.finish-date	2019-07-12
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-08
pubs.volume	2019-July
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Online learning for imbalanced streaming data is an important and challenging problem for many classification tasks in the machine learning research field. Traditional online learning algorithms are mainly focused on classification tasks with balanced data, and with little consideration about the characteristics of imbalanced streaming data. In this paper, we propose a novel online learning algorithm called Truncated Gradient Confidence-Weighted (TGCW), which integrate the truncated gradient algorithm with the confidence weighted algorithm together to improve the feature selection ability while reducing the dimensions of imbalanced streaming data effectively. We study a number of classification tasks with various imbalance data ratio including the pedestrian detection application and compare the performance of the TGCW algorithm with traditional online learning algorithms, and empirical results show that the TGCW algorithm can achieve better performance consistently than other baseline approaches.

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http://hdl.handle.net/10453/141211