Probabilistic Active Learning for Active Class Selection
This work addresses the challenge of optimizing classifier performance with minimal oracle requests in machine learning, representing an incremental advancement over existing ACS algorithms.
The paper tackles the problem of active class selection (ACS) by proposing PAL-ACS, which transforms ACS into an active learning task using pseudo instances to estimate instance usefulness, resulting in improved classification performance by preferring difficult classes.
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.