Probabilistic Active Learning for Active Class Selection
Abstract
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.
Cite
@article{arxiv.2108.03891,
title = {Probabilistic Active Learning for Active Class Selection},
author = {Daniel Kottke and Georg Krempl and Marianne Stecklina and Cornelius Styp von Rekowski and Tim Sabsch and Tuan Pham Minh and Matthias Deliano and Myra Spiliopoulou and Bernhard Sick},
journal= {arXiv preprint arXiv:2108.03891},
year = {2021}
}