Active learning algorithm through the lens of rejection arguments

TL;DR

This paper introduces a novel active learning algorithm leveraging rejection arguments to efficiently identify uncertain regions, backed by theoretical analysis and empirical validation on diverse datasets.

Contribution

It proposes a new active learning method using rejection classification, with proven theoretical advantages and demonstrated empirical effectiveness.

Findings

The algorithm effectively identifies uncertain regions.

Theoretical benefits are established under classical assumptions.

Empirical results show good performance across datasets.

Abstract

Active learning is a paradigm of machine learning which aims at reducing the amount of labeled data needed to train a classifier. Its overall principle is to sequentially select the most informative data points, which amounts to determining the uncertainty of regions of the input space. The main challenge lies in building a procedure that is computationally efficient and that offers appealing theoretical properties; most of the current methods satisfy only one or the other. In this paper, we use the classification with rejection in a novel way to estimate the uncertain regions. We provide an active learning algorithm and prove its theoretical benefits under classical assumptions. In addition to the theoretical results, numerical experiments have been carried out on synthetic and non-synthetic datasets. These experiments provide empirical evidence that the use of rejection arguments in our active learning algorithm is beneficial and allows good performance in various statistical situations.