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In machine learning, active class selection (ACS) algorithms aim to activelyselect a class and ask the oracle to provide an instance for that class tooptimize a classifier's performance while minimizing the number of requests. Inthis paper, we propose a new algorithm (PAL-ACS) that transforms the ACSproblem into an active learning task by introducing pseudo instances. These areused to estimate the usefulness of an upcoming instance for each class usingthe performance gain model from probabilistic active learning. Our experimentalevaluation (on synthetic and real data) shows the advantages of our algorithmcompared to state-of-the-art algorithms. It effectively prefers the sampling ofdifficult classes and thereby improves the classification performance.

Probabilistic Active Learning for Active Class Selection