A Selective Sampling Strategy for Label Ranking

Abstract : We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen’s generalization bounds using unlabeled data [7], initially developed in the context of classification. The bounds we obtain suggest a selective sampling strategy provided that a sufficiently, yet reasonably large initial labeled dataset is provided. Experiments on Information Retrieval corpora from automatic text summarization and question/answering show that the proposed approach allows to substantially reduce the labeling effort in comparison to random and heuristic-based sampling strategies.
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Massih-Reza Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari. A Selective Sampling Strategy for Label Ranking. European Conference on Machine Learning (ECML'06), Sep 2006, Berlin, Germany. pp.18-29, ⟨10.1007/11871842_7⟩. ⟨hal-01337085⟩



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