Vote-Based LELC for Positive and Unlabeled Textual Data Streams

UTSePress Research/Manakin Repository

Search UTSePress Research

Advanced Search


My Account

Show simple item record Liu, Bo en_US Xiao, Yan Shan en_US Cao, Longbing en_US Yu, Philip en_US
dc.contributor.editor Wei Fan, Wynne Hsu, Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu en_US 2012-02-02T11:08:06Z 2012-02-02T11:08:06Z 2010 en_US
dc.identifier 2010001678 en_US
dc.identifier.citation Liu Bo et al. 2010, 'Vote-Based LELC for Positive and Unlabeled Textual Data Streams', , IEEE Computer Society Conference Publishing Services (CPS), USA, , pp. 951-958. en_US
dc.identifier.issn 978-0-7695-4257-7 en_US
dc.identifier.other E1 en_US
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. en_US
dc.language English en_US
dc.publisher IEEE Computer Society Conference Publishing Services (CPS) en_US
dc.relation.isbasedon en_US
dc.title Vote-Based LELC for Positive and Unlabeled Textual Data Streams en_US
dc.parent 2010 IEEE International Conference on Data Mining Workshops (ICDMW) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 951 en_US
dc.identifier.endpage 958 en_US FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 080109 en_US
dc.personcode 100970 en_US
dc.personcode 10783696 en_US
dc.personcode 034535 en_US
dc.personcode 107211 en_US
dc.percentage 50 en_US 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 20101214 en_US
dc.location.activity Sydney, NSW, Australia en_US
dc.description.keywords Positive and Unlabeled Learning, Data Streams en_US
dc.staffid 107211 en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record