A Comparative Survey : Benchmarking for Pool-based Active Learning

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

21 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21)
EditorsZhi-Hua Zhou
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4679-4686
ISBN (Electronic)978-0-9992411-9-6
Publication statusPublished - Aug 2021

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Title30th International Joint Conference on Artificial Intelligence (IJCAI 2021)
LocationVirtual, Online
PlaceCanada
Period19 - 27 August 2021

Abstract

Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm aims to achieve good accuracy with fewer training samples by interactively querying the oracles to label new data points. Pool-based AL is well-motivated in many ML tasks, where unlabeled data is abundant, but their labels are hard or costly to obtain. Although many pool-based AL methods have been developed, some important questions remain unanswered such as how to: 1) determine the current state-of-the-art technique; 2) evaluate the relative benefit of new methods for various properties of the dataset; 3) understand what specific problems merit greater attention; and 4) measure the progress of the field over time. In this paper, we survey and compare various AL strategies used in both recently proposed and classic highly-cited methods. We propose to benchmark pool-based AL methods with a variety of datasets and quantitative metric, and draw insights from the comparative empirical results.

Citation Format(s)

A Comparative Survey: Benchmarking for Pool-based Active Learning. / Zhan, Xueying; Liu, Huan; Li, Qing et al.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) . ed. / Zhi-Hua Zhou. International Joint Conferences on Artificial Intelligence, 2021. p. 4679-4686 (IJCAI International Joint Conference on Artificial Intelligence).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review