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Computational Intelligence and Neuroscience
Volume 2015, Article ID 973696, 17 pages
Research Article

Optimism in Active Learning

1CentraleSupélec, MaLIS Research Group, 57070 Metz, France
2GeorgiaTech-CNRS UMI 2958, 57070 Metz, France
3Université de Lille-CRIStAL UMR 9189, SequeL Team, 59650 Villeneuve d’Ascq, France
4Institut Universitaire de France (IUF), 75005 Paris, France

Received 15 April 2015; Accepted 12 August 2015

Academic Editor: Francesco Camastra

Copyright © 2015 Timothé Collet and Olivier Pietquin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism int the Face of Uncertainty, has proven very efficient these last years. This paper introduces three algorithms for the active learning problem in classification using Optimism in the Face of Uncertainty. Experiments lead on built-in problems and real world datasets demonstrate that they compare positively to state-of-the-art methods.