Transfer active learning

Zhu, Z; Zhu, X; Ye, Y; Guo, YF; Xue, X

Transfer active learning

Zhu, Z Zhu, X Ye, Y Guo, YF Xue, X

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 2169 - 2172
Issue Date:: 2011-12-13

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Field	Value	Language
dc.contributor.author	Zhu, Z	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Ye, Y	en_US
dc.contributor.author	Guo, YF	en_US
dc.contributor.author	Xue, X	en_US
dc.date.issued	2011-12-13	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 2169 - 2172	en_US
dc.identifier.isbn	9781450307178	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19186
dc.description.abstract	Active learning traditionally assumes that labeled and unlabeled samples are subject to the same distributions and the goal of an active learner is to label the most informative unlabeled samples. In reality, situations may exist that we may not have unlabeled samples from the same domain as the labeled samples (i.e. target domain), whereas samples from auxiliary domains might be available. Under such situations, an interesting question is whether an active learner can actively label samples from auxiliary domains to benefit the target domain. In this paper, we propose a transfer active learning method, namely Transfer Active SVM (TrAcSVM), which uses a limited number of target instances to iteratively discover and label informative auxiliary instances. TrAcSVM employs an extended sigmoid function as instance weight updating approach to adjust the models for prediction of (newly arrived) target data. Experimental results on real-world data sets demonstrate that TrAcSVM obtains better efficiency and prediction accuracy than its peers. © 2011 ACM.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2063576.2063918	en_US
dc.title	Transfer active learning	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Glasgow, Scotland	en_US
pubs.embargo.period	Not known	en_US
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Active learning traditionally assumes that labeled and unlabeled samples are subject to the same distributions and the goal of an active learner is to label the most informative unlabeled samples. In reality, situations may exist that we may not have unlabeled samples from the same domain as the labeled samples (i.e. target domain), whereas samples from auxiliary domains might be available. Under such situations, an interesting question is whether an active learner can actively label samples from auxiliary domains to benefit the target domain. In this paper, we propose a transfer active learning method, namely Transfer Active SVM (TrAcSVM), which uses a limited number of target instances to iteratively discover and label informative auxiliary instances. TrAcSVM employs an extended sigmoid function as instance weight updating approach to adjust the models for prediction of (newly arrived) target data. Experimental results on real-world data sets demonstrate that TrAcSVM obtains better efficiency and prediction accuracy than its peers. © 2011 ACM.

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http://hdl.handle.net/10453/19186