Distribution-Aware Online Classifiers
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1427-1431.
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.
@inproceedings{ijcai2011p241,
title = {Distribution-Aware Online Classifiers},
author = {Nguyen, Tam T. and Chang, Kuiyu and Hui, Siu Cheung},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1427--1431},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-241},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-241},
}
TY - CPAPER TI - Distribution-Aware Online Classifiers AU - Nguyen, Tam T. AU - Chang, Kuiyu AU - Hui, Siu Cheung T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1427 EP - 1431 DO - 10.5591/978-1-57735-516-8/IJCAI11-241 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-241 ER -Copied
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