Active Learning From Stream Data Using Optimal Weight Classifier Ensemble

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Show simple item record Zhu, X Zhang, P Lin, X Shi, Y 2012-02-02T04:23:38Z 2010-01
dc.identifier.citation Ieee Transactions On Systems Man And Cybernetics Part B-Cybernetics, 2010, 40 (6), pp. 1607 - 1621
dc.identifier.issn 1083-4419
dc.identifier.other C1 en_US
dc.description.abstract In this paper, we propose a new research problem on active learning from data streams, where data volumes grow continuously, and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. To tackle the technical challenges raised by the dynamic nature of the stream data, i.e., increasing data volumes and evolving decision concepts, we propose a classifierensemble- based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensembleâs variance directly corresponds to its error rate, and reducing a classifier ensembleâs variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward theminimization of the variance of the underlying classifier ensemble. Accordingly, we introduce a minimum-variance (MV) principle to guide the instance labeling process for data streams. In addition, we derive an optimal-weight calculationmethod to determine the weight values for the classifier ensemble. The MV principle and the optimal weighting module are combined to build an active learning framework for data streams. Experimental results on synthetic and real-world data demonstrate the performance of the proposed work in comparison with other approaches.
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc
dc.relation.isbasedon 10.1109/TSMCB.2010.2042445
dc.title Active Learning From Stream Data Using Optimal Weight Classifier Ensemble
dc.type Journal Article
dc.parent Ieee Transactions On Systems Man And Cybernetics Part B-Cybernetics
dc.journal.volume 6
dc.journal.volume 40
dc.journal.number 6 en_US
dc.publocation Piscataway en_US
dc.identifier.startpage 1607 en_US
dc.identifier.endpage 1621 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 0102 Applied Mathematics
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 107283
dc.personcode 120662
dc.percentage 50 en_US Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity ISI:000284364400016 en_US
dc.description.keywords Active learning, classifier ensemble, stream data en_US
dc.description.keywords Humans
dc.description.keywords Manipulation, Chiropractic
dc.description.keywords Manipulation, Osteopathic
dc.description.keywords Prevalence
dc.description.keywords Longitudinal Studies
dc.description.keywords Health Status
dc.description.keywords Middle Aged
dc.description.keywords Women's Health
dc.description.keywords Rural Population
dc.description.keywords Urban Population
dc.description.keywords Health Services
dc.description.keywords Australia
dc.description.keywords Female
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 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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