Active learning using uncertainty information

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 i...

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Bibliographic Details
Published in2016 23rd International Conference on Pattern Recognition (ICPR) pp. 2646 - 2651
Main Authors Yazhou Yang, Loog, Marco
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Labeling
Learning systems
Linear programming
Logistics
Measurement uncertainty
Pattern recognition
Uncertainty
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Summary: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.
DOI:10.1109/ICPR.2016.7900034