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Active Learning for Classification: An Optimistic Approach


In this paper, we propose to reformulate the active learning problem occurring in classification as a sequential decision making problem. We particularly focus on the problem of dynamically allocating a fixed budget of samples. This raises the problem of the trade off between exploration and exploitation which is traditionally addressed in the framework of the multi-armed bandits theory. Based on previous work on bandit theory applied to active learning for regression, we introduce four novel algorithms for solving the online allocation of the budget in a classification problem. Experiments on a generic classification problem demonstrate that these new algorithms compare positively to state-of-the-art methods.
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hal-01107508 , version 1 (20-01-2015)



Timothé Collet, Olivier Pietquin. Active Learning for Classification: An Optimistic Approach. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2014), Dec 2014, Orlando, United States. pp.1 - 8, ⟨10.1109/ADPRL.2014.7010610⟩. ⟨hal-01107508⟩
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