Vague One-Class Learning for Data Streams

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dc.contributor.author Zhu, X
dc.contributor.author Wu, X
dc.contributor.author Zhang, C
dc.contributor.editor Wei, W
dc.contributor.editor Hillol, K
dc.contributor.editor Sanjay, R
dc.contributor.editor Yu, PS
dc.contributor.editor Xindong, W
dc.date.accessioned 2012-03-06T10:46:29Z
dc.date.issued 2009-01
dc.identifier.citation Proceedings of the 9th IEEE International Conference on Data Mining (ICDM-09), 2009, pp. 657 - 666
dc.identifier.isbn 978-0-7695-3895-2
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17494
dc.description.abstract In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any time. To solve this problem, we propose a Vague One-Class Learning (VOCL) framework which employs a double weighting approach, at both instance and classifier levels, to build an ensembling framework for learning. At instance level, both local and global filterings are considered for instance weight adjustment. Two solutions are proposed to take instance weight values into the classifier training process. At classifier level, a weight value is assigned to each classifier of the ensemble to ensure that learning can quickly adapt to usersâ interests. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL framework significantly outperforms other methods for vaguely labeled one-class data streams.
dc.publisher IEEE Computer Society
dc.relation.isbasedon 10.1109/ICDM.2009.70
dc.title Vague One-Class Learning for Data Streams
dc.type Conference Proceeding
dc.parent Proceedings of the 9th IEEE International Conference on Data Mining (ICDM-09)
dc.journal.number en_US
dc.publocation Washington, DC, USA en_US
dc.publocation Washington, DC, USA
dc.publocation Washington, DC, USA
dc.identifier.startpage 657 en_US
dc.identifier.endpage 666 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Data Mining
dc.conference IEEE International Conference on Data Mining
dc.for 0806 Information Systems
dc.personcode 100507
dc.personcode 011221
dc.personcode 107283
dc.percentage 100 en_US
dc.classification.name Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Conference on Data Mining en_US
dc.date.activity 20091206 en_US
dc.date.activity 2009-12-06
dc.date.activity 2009-12-06
dc.location.activity Miami, Florida en_US
dc.location.activity Miami, Florida
dc.location.activity Miami, Florida
dc.description.keywords Stream data, one-class learning, vague labeling en_US
dc.description.keywords Stream data, one-class learning, vague labeling
dc.description.keywords Stream data, one-class learning, vague labeling
pubs.embargo.period Not known
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 - Quantum Computation and Intelligent Systems


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