Probabilistic Active Learning for Active Class Selection

TL;DR

This paper introduces PAL-ACS, a novel algorithm that transforms active class selection into an active learning problem using pseudo instances, leading to improved classification by focusing on difficult classes.

Contribution

The paper presents PAL-ACS, a new method that leverages pseudo instances and probabilistic models to enhance class selection in active learning scenarios.

Findings

Outperforms state-of-the-art ACS algorithms on synthetic and real datasets.

Effectively prioritizes difficult classes, improving classifier performance.

Demonstrates advantages of pseudo instances in 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.