Active Learning Using Uncertainty Information

Yazhou Yang, Marco Loog

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)

Abstract

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of real-world datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications.
Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages2646-2651
Number of pages6
ISBN (Electronic)978-1-5090-4847-2
ISBN (Print)978-1-5090-4848-9
DOIs
Publication statusPublished - 2016
EventICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23

Conference

ConferenceICPR 2016
CountryMexico
CityCancún
Period4/12/168/12/16

Keywords

  • Linear programming
  • Uncertainty
  • Labeling
  • Logistics
  • Learning systems
  • Measurement uncertainty
  • Pattern recognition

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  • Cite this

    Yang, Y., & Loog, M. (2016). Active Learning Using Uncertainty Information. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 2646-2651). IEEE. https://doi.org/10.1109/ICPR.2016.7900034