Proceedings

Distribution-Aware Online Classifiers

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1427-1431.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-241

Abstract

We propose a family of Passive-Aggressive Mahalanobis (PAM) algorithms, which are incremental (online) binary classifiers that consider the distribution of data. PAM is in fact a generalization of the Passive-Aggressive (PA) algorithms to handle data distributions that can be represented by a covariance matrix. The update equations for PAM are derived and theoretical error loss bounds computed. We benchmarked PAM against the original PA-I, PA-II, and Confidence Weighted (CW) learning. Although PAM somewhat resembles CW in its update equations, PA minimizes differences in the weights while CW minimizes differences in weight distributions. Results on 8 classification datasets, which include a real-life micro-blog sentiment classification task, show that PAM consistently outperformed its competitors, most notably CW. This shows that a simple approach like PAM is more practical in real-life classification tasks, compared to more elegant and sophisticated approaches like CW.

Tam T. Nguyen, Kuiyu Chang, and Siu Cheung Hui. 2011. Distribution-Aware Online Classifiers. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1427–1431. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-241
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