Vote-Based LELC for Positive and Unlabeled Textual Data Streams

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dc.contributor.author Liu, B
dc.contributor.author Xiao, Y
dc.contributor.author Cao, L
dc.contributor.author Yu, P
dc.contributor.editor Fan, W
dc.contributor.editor Hsu, W
dc.contributor.editor Webb, GI
dc.contributor.editor Liu, B
dc.contributor.editor Zhang, C
dc.contributor.editor Gunopulos, D
dc.contributor.editor Wu, X
dc.date.accessioned 2012-02-02T11:08:06Z
dc.date.issued 2010-01
dc.identifier.citation 2010 IEEE International Conference on Data Mining Workshops (ICDMW), 2010, pp. 951 - 958
dc.identifier.isbn 978-0-7695-4257-7
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16271
dc.description.abstract In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works in three steps. In the first step, we extract representative documents from unlabeled data and assign a vote score to each document. The assigned vote score reflects the degree of belongingness of an example towards its corresponding class. In the second step, the extracted representative examples, together with their vote scores, are incorporated into a learning phase to build an SVM-based classifier. In the third step, we propose the usage of an ensemble classifier to cope with concept drift involved in the textual data stream environment. Our developed approach aims at improving the performance of LELC by rendering examples to contribute differently to the construction of the classifier according to their vote scores. Extensive experiments on textual data streams have demonstrated that vote-based LELC outperforms the original LELC method.
dc.publisher IEEE Computer Society Conference Publishing Services (CPS)
dc.relation.isbasedon 10.1109/ICDMW.2010.201
dc.title Vote-Based LELC for Positive and Unlabeled Textual Data Streams
dc.type Conference Proceeding
dc.parent 2010 IEEE International Conference on Data Mining Workshops (ICDMW)
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 951 en_US
dc.identifier.endpage 958 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Data Mining
dc.for 0804 Data Format
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 034535
dc.personcode 100970
dc.personcode 107211
dc.percentage 50 en_US
dc.classification.name Pattern Recognition and Data Mining 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 20101214 en_US
dc.date.activity 2010-12-14
dc.location.activity Sydney, NSW, Australia en_US
dc.location.activity ISI:000248565800001
dc.description.keywords Positive and Unlabeled Learning, Data Streams en_US
dc.description.keywords NA
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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