Work

Active learning algorithm through the lens of rejection arguments

Year: 2023

Type: article

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 are 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.

Source: Machine Learning

DOI: 10.1007/s10994-023-06494-8

Authors: Christophe Denis, Mohamed Hebiri, Boris Ndjia Njike, Xavier Siebert

Institutions: Centre National de la Recherche Scientifique, Université Paris-Est Créteil, Laboratoire d’Analyse et de Mathématiques Appliquées, Université Gustave Eiffel, University of Mons

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funder: Université de Mons