# Probabilistic Active Learning for Active Class Selection

arXiv: https://arxiv.org/abs/2108.03891 · HTML: https://mlsanity.com/paper/2108.03891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Active Class Selection for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2307.02641/) (2023)
- [Dependency-aware Maximum Likelihood Estimation for Active Learning](https://mlsanity.com/paper/2503.05969/) (2025)

## Abstract (arXiv)

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.