Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Exploiting domain structure for named entity recognition
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Partitioned logistic regression for spam filtering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Hierarchical Bayesian domain adaptation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A theory of learning from different domains
Machine Learning
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
The necessity of combining adaptation methods
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Instance selection and instance weighting for cross-domain sentiment classification via PU learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
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.