Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier

The objective of pool-based incremental active learning is to choose a sample to label from a pool of unlabeled samples in an incremental manner so that the generalization error is minimized. In this scenario, the generalization error often hits a minimum in the middle of the incremental active lear...

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Veröffentlicht in:IEICE Transactions on Information and Systems 2012/08/01, Vol.E95.D(8), pp.2065-2073
Hauptverfasser: KOBAYASHI, Tsubasa, SUGIYAMA, Masashi
Format: Artikel
Sprache:eng
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Zusammenfassung:The objective of pool-based incremental active learning is to choose a sample to label from a pool of unlabeled samples in an incremental manner so that the generalization error is minimized. In this scenario, the generalization error often hits a minimum in the middle of the incremental active learning procedure and then it starts to increase. In this paper, we address the problem of early labeling stopping in probabilistic classification for minimizing the generalization error and the labeling cost. Among several possible strategies, we propose to stop labeling when the empirical class-posterior approximation error is maximized. Experiments on benchmark datasets demonstrate the usefulness of the proposed strategy.
ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.E95.D.2065