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Classification Active Learning Based on Mutual Information

Sourati, Jamshid and Akcakaya, Murat and Dy, Jennifer and Leen, Todd and Erdogmus, Deniz (2016) Classification Active Learning Based on Mutual Information. Entropy, 18 (2). ISSN 1099-4300

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Selecting a subset of samples to label from a large pool of unlabeled data points, such that a sufficiently accurate classifier is obtained using a reasonably small training set is a challenging, yet critical problem. Challenging, since solving this problem includes cumbersome combinatorial computations, and critical, due to the fact that labeling is an expensive and time-consuming task, hence we always aim to minimize the number of required labels. While information theoretical objectives, such as mutual information (MI) between the labels, have been successfully used in sequential querying, it is not straightforward to generalize these objectives to batch mode. This is because evaluation and optimization of functions which are trivial in individual querying settings become intractable for many objectives when we are to select multiple queries. In this paper, we develop a framework, where we propose efficient ways of evaluating and maximizing the MI between labels as an objective for batch mode active learning. Our proposed framework efficiently reduces the computational complexity from an order proportional to the batch size, when no approximation is applied, to the linear cost. The performance of this framework is evaluated using data sets from several fields showing that the proposed framework leads to efficient active learning for most of the data sets.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Sourati, Jamshid
Akcakaya, Muratakcakaya@pitt.eduakcakaya
Dy, Jennifer
Leen, Todd
Erdogmus, Deniz
Date: 5 February 2016
Date Type: Publication
Journal or Publication Title: Entropy
Volume: 18
Number: 2
Publisher: MDPI AG
DOI or Unique Handle: 10.3390/e18020051
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Refereed: Yes
Uncontrolled Keywords: active learning, mutual information, submodular maximization, classification
ISSN: 1099-4300
Official URL:
Funders: NSF, NIH
Article Type: Research Article
Date Deposited: 15 Mar 2021 16:03
Last Modified: 15 Mar 2021 16:03


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