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Cost effective multi-label active learning via querying subexamples
conference contribution
posted on 2018-01-01, 00:00 authored by X Chen, G Yu, C Domeniconi, J Wang, Z Li, Zili ZhangZili ZhangMulti-label active learning addresses the scarce labeled example problem by querying the most valuable unlabeled examples, or example-label pairs, to achieve a better performance with limited query cost. Current multi-label active learning methods require the scrutiny of the whole example in order to obtain its annotation. In contrast, one can find positive evidence with respect to a label by examining specific patterns (i.e., subexample), rather than the whole example, thus making the annotation process more efficient. Based on this observation, we propose a novel two-stage cost effective multi-label active learning framework, called CMAL. In the first stage, a novel example-label pair selection strategy is introduced. Our strategy leverages label correlation and label space sparsity of multi-label examples to select the most uncertain example-label pairs. Specifically, the unknown relevant label of an example can be inferred from the correlated labels that are already assigned to the example, thus reducing the uncertainty of the unknown label. In addition, the larger the number of relevant examples of a particular label, the smaller the uncertainty of the label is. In the second stage, CMAL queries the most plausible positive subexample-label pairs of the selected example-label pairs. Comprehensive experiments on multi-label datasets collected from different domains demonstrate the effectiveness of our proposed approach on cost effective queries. We also show that leveraging label correlation and label sparsity contribute to saving costs.
History
Event
IEEE Computer Society. Conference (2018 : Singapore)Series
IEEE Computer Society ConferencePagination
905 - 910Publisher
Institute of Electrical and Electronics EngineersLocation
SingaporePlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-11-17End date
2018-11-20ISSN
1550-4786ISBN-13
9781538691588Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICDM 2018 : Proceedings of the 2018 IEEE International Conference on Data MiningUsage metrics
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