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

• KOBAYASHI Tsubasa

  Tokyo Institute of Technology

• SUGIYAMA Masashi

  Tokyo Institute of Technology

Abstract

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.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E95.D (8), 2065-2073, 2012

  The Institute of Electronics, Information and Communication Engineers

Keywords

• pool-based incremental active learning
• early stopping
• least-squares probabilistic classifier
• semi-supervised learning

Details

• CRID: 1390282679354940416
• NII Article ID: 10031126715
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e95.d.2065
• BIBCODE: 2012IEITI..95.2065K
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E95.D/8/E95.D_2065/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed