Fuzzy rough sets based uncertainty measuring for stream based active learning
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - International Conference on Machine Learning and Cybernetics |
Pages | 282-288 |
Volume | 1 |
Publication status | Published - 2012 |
Publication series
Name | |
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Volume | 1 |
ISSN (Print) | 2160-133X |
ISSN (electronic) | 2160-1348 |
Conference
Title | 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 |
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Place | China |
City | Xian, Shaanxi |
Period | 15 - 17 July 2012 |
Link(s)
Abstract
Active learning methods put their efforts on selecting and labeling the most informative examples out of a large amount of unlabeled ones. It is performed in uncertain environments where the learner is required to make some decisions on the observed examples. However, existing algorithms do not have a good formulation to evaluate the example's uncertainty by considering the inconsistency between conditional features and decision labels, while this inconsistency has been taken into account by fuzzy rough sets. Therefore, a fuzzy rough sets based active learning algorithm with stream based settings is proposed in this work. The lower approximations in fuzzy rough sets are used to compute the memberships of the unlabeled example, and the uncertainty is then used for decision. Experimental comparisons with other existing approaches demonstrate the effectiveness of the proposed algorithm. © 2012 IEEE.
Research Area(s)
- Active learning, Fuzzy rough sets, Membership, Support vector machine, Uncertainty
Citation Format(s)
Fuzzy rough sets based uncertainty measuring for stream based active learning. / Wang, Ran; Kwong, Sam; Chen, Degang et al.
Proceedings - International Conference on Machine Learning and Cybernetics. Vol. 1 2012. p. 282-288 6358926.
Proceedings - International Conference on Machine Learning and Cybernetics. Vol. 1 2012. p. 282-288 6358926.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review