Constrained bayesian active learning of linear classifier

In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle possible. Additionally' a constraint is introduced into this interactive learning process which limits the percentage of the samples from one “unwanted” class under a certain threshold. An optimal AL solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP) and its performance is demonstrated through numerical simulations.