title: Optimism in Active Learning.
abstract: 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 in 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.
authors.affiliation: CentraleSupélec, MaLIS Research Group, 57070 Metz, France ; GeorgiaTech-CNRS UMI 2958, 57070 Metz, France.
authors.forename: Timothé
authors.initials: T
authors.lastname: Collet
authors.affiliation: Université de Lille-CRIStAL UMR 9189, SequeL Team, 59650 Villeneuve d'Ascq, France ; Institut Universitaire de France (IUF), 75005 Paris, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Pietquin
mesh_terms: D000465:Algorithms; D001290:Attitude; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D000069550:Machine Learning; D015203:Reproducibility of Results; D035501:Uncertainty