Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later-Reference-Cited by-同舟云学术

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Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

Published:2024 Issue: Volume:12 Page:21129-21146
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mahdi Osama A.¹^ORCID,Ali Nawfal²,Pardede Eric³^ORCID,Alazab Ammar⁴^ORCID,Al-Quraishi Tahsien⁵,Das Bhagwan¹^ORCID

Affiliation:

1. School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, VIC, Australia

2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

3. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia

4. Centre for Artificial Intelligence and Optimization, DCT, Torrens University, Sydney, NSW, Australia

5. Higher Education Department, Victorian Institute of Technology (VIT), Melbourne, VIC, Australia

Funder

Melbourne Institute of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10414782.pdf?arnumber=10414782

Reference85 articles.

1. Diversity measures as new concept drift detection methods in data stream mining;Mahdi,2020

2. Knowledge Discovery from Data Streams

3. Stream Data Mining: Platforms, Algorithms, Performance Evaluators and Research Trends

4. Learning under concept drift: An overview;Žliobaitė;arXiv:1010.4784,2010

5. Accumulating regional density dissimilarity for concept drift detection in data streams

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