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CIKM
2010
Springer

Combining link and content for collective active learning

8 years 9 months ago
Combining link and content for collective active learning
In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query the user in order to improve the accuracy of the learned classification model. We perform a theoretical investigation of the problem and present three criteria (i.e., minimum redundancy, maximum uncertainty and maximum impact) to quantify the informativeness of a set of selected instances. We define an objective function based on the three criteria and present an efficient algorithm to optimize the objective function with a bounded approximation rate. Experimental results on a real-world data sets demonstrate the effectiveness of our proposed approach. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Text Mining; I.2.6 [Artificial Intelligence]: Learning General Terms Algorithms, Experimentation Keywords collective active learning, link, document classification
Lixin Shi, Yuhang Zhao, Jie Tang
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where CIKM
Authors Lixin Shi, Yuhang Zhao, Jie Tang
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