  In machine learning, active class selection (ACS) algorithms aim to actively
select a class and ask the oracle to provide an instance for that class to
optimize a classifier's performance while minimizing the number of requests. In
this paper, we propose a new algorithm (PAL-ACS) that transforms the ACS
problem into an active learning task by introducing pseudo instances. These are
used to estimate the usefulness of an upcoming instance for each class using
the performance gain model from probabilistic active learning. Our experimental
evaluation (on synthetic and real data) shows the advantages of our algorithm
compared to state-of-the-art algorithms. It effectively prefers the sampling of
difficult classes and thereby improves the classification performance.
