Date of Original Version
© ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 25th international Conference on Machine Learning (Helsinki, Finland, July 05 - 09, 2008). ICML '08, vol. 307. ACM, New York, NY, 248-255 http://doi.acm.org/10.1145/1390156.1390188
Abstract or Description
Learning to rank is becoming an increasingly popular research area in machine learning. The ranking problem aims to induce an ordering or preference relations among a set of instances in the input space. However, collecting labeled data is growing into a burden in many rank applications since labeling requires eliciting the relative ordering over the set of alternatives. In this paper, we propose a novel active learning framework for SVM-based and boosting-based rank learning. Our approach suggests sampling based on maximizing the estimated loss differential over unlabeled data. Experimental results on two benchmark corpora show that the proposed model substantially reduces the labeling effort, and achieves superior performance rapidly with as much as 30% relative improvement over the margin-based sampling baseline.
Proceedings of the 25th international Conference on Machine Learning. ICML '08, 248-255.