Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Active learning for data streams: a survey
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.ORCID iD: 0000-0001-6664-9038
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.ORCID iD: 0000-0003-4222-9631
2024 (English)In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 113, no 1, p. 185-239Article, review/survey (Refereed) Published
Abstract [en]

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 113, no 1, p. 185-239
Keywords [en]
Bandits, Concept drift, Data streams, Experimental design, Online active learning, Online learning, Query strategies, Selective sampling, Stream-based active learning, Unlabeled data
National Category
Computer Sciences
Research subject
Quality Technology and Logistics
Identifiers
URN: urn:nbn:se:ltu:diva-103014DOI: 10.1007/s10994-023-06454-2Scopus ID: 2-s2.0-85177180685OAI: oai:DiVA.org:ltu-103014DiVA, id: diva2:1815468
Note

Validerad;2024;Nivå 2;2024-04-02 (hanlid);

Funder: DTU Strategic Alliances Fund;

Full text license: CC BY 4.0

Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-04-02Bibliographically approved

Open Access in DiVA

fulltext(2175 kB)16 downloads
File information
File name FULLTEXT02.pdfFile size 2175 kBChecksum SHA-512
b71899cf06dd9b457c64f25fa2dd9976e040bf9ac8c1c6686d0f7fc9048507394d01432b20a90340ab7b89ba548af7553d09b59abe931bb3f2a7b5eb34025680
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Kulahci, Murat

Search in DiVA

By author/editor
Cacciarelli, DavideKulahci, Murat
By organisation
Business Administration and Industrial Engineering
In the same journal
Machine Learning
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 75 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 324 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf