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A Selective Sampling Strategy for Label Ranking (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Amini, Massih Usunier, Nicolas Laviolette, François Lacasse, Alexandre Gallinari, Patrick |
| 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. |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Selective Sampling Strategy Label Ranking Predefined Set Label Ranking Function Unlabeled Data Ri Inen Generalization Active Learning Compression Framework Heuristic-based Sampling Strategy Automatic Text Summarization Input Instance Labeling Effort Total Order Information Retrieval Corpus |
| Content Type | Text |
