Domain Adaptation with Ensemble of Feature Groups
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1458-1463.
Abstract
We present a novel approach for domain adaptation based on feature grouping and re-weighting. Our algorithm operates by creating an ensemble of multiple classifiers, where each classifier is trained on one particular feature group. Faced with the distribution change involved in domain change, different feature groups exhibit different cross-domain prediction abilities. Herein, ensemble models provide us the flexibility of tuning the weights of corresponding classifiers in order to adapt to the new domain. Our approach is supported by a solid theoretical analysis based on the expressiveness of ensemble classifiers, which allows trading-off errors across source and target domains. Moreover, experimental results on sentiment classification and spam detection show that our approach not only outperforms the baseline method, but is also superior to other state-of-the-art methods.
@inproceedings{ijcai2011p246,
title = {Domain Adaptation with Ensemble of Feature Groups},
author = {Samdani, Rajhans and Yih, Wen-tau},
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 = {1458--1463},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-246},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-246},
}
TY - CPAPER TI - Domain Adaptation with Ensemble of Feature Groups AU - Samdani, Rajhans AU - Yih, Wen-tau 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 - 1458 EP - 1463 DO - 10.5591/978-1-57735-516-8/IJCAI11-246 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-246 ER -Copied
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