Proceedings

Active Online Classification via Information Maximization

Noam Slonim, Elad Yom-Tov, Koby Crammer

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1498-1503.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-252

Abstract

We propose an online classification approach for co-occurrence data which is based on a simple information theoretic principle. We further show how to properly estimate the uncertainty associated with each prediction of our scheme and demonstrate how to exploit these uncertainty estimates. First, in order to abstain highly uncertain predictions. And second, within an active learning framework, in order to preserve classification accuracy while substantially reducing training set size. Our method is highly efficient in terms of run-time and memory footprint requirements. Experimental results in the domain of text classification demonstrate that the classification accuracy of our method is superior or comparable to other state-of-the-art online classification algorithms.

Noam Slonim, Elad Yom-Tov, and Koby Crammer. 2011. Active Online Classification via Information Maximization. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1498–1503. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-252
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