Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Concept Drift�s issue is a decisive problem of online machine learning, which causes massive performance degradation in the analysis. The Concept Drift is observed when data�s statistical properties vary at a different time step and deteriorate the trained model�s accuracy and make them ineffe...

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Main Authors: Hashmani, M.A., Jameel, S.M., Rehman, M., Inoue, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23350 /
Published: Exeley Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101082701&doi=10.21307%2fijssis-2020-029&partnerID=40&md5=50742a63c76aac5f02af91df2a5ed9ae
http://eprints.utp.edu.my/23350/
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spelling utp-eprints.233502021-08-19T07:24:06Z Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review Hashmani, M.A. Jameel, S.M. Rehman, M. Inoue, A. Concept Drift�s issue is a decisive problem of online machine learning, which causes massive performance degradation in the analysis. The Concept Drift is observed when data�s statistical properties vary at a different time step and deteriorate the trained model�s accuracy and make them ineffective. However, online machine learning has significant importance to fulfill the demands of the current computing revolution. Moreover, it is essential to understand the existing Concept Drift handling techniques to determine their associated pitfalls and propose robust solutions. This study attempts to summarize and clarify the empirical pieces of evidence of the Concept Drift issue and assess its applicability to meet the current computing revolution. Also, this study provides a few possible research directions and practical implications of Concept Drift handling. © 2020 Authors. This work is licensed under the Creative Commons Attribution-Non- Commercial-NoDerivs 4.0 License https://creativecommons.org/licenses/by-nc-nd/4.0/ Exeley Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101082701&doi=10.21307%2fijssis-2020-029&partnerID=40&md5=50742a63c76aac5f02af91df2a5ed9ae Hashmani, M.A. and Jameel, S.M. and Rehman, M. and Inoue, A. (2020) Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review. International Journal on Smart Sensing and Intelligent Systems, 13 (1). pp. 1-16. http://eprints.utp.edu.my/23350/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Concept Drift�s issue is a decisive problem of online machine learning, which causes massive performance degradation in the analysis. The Concept Drift is observed when data�s statistical properties vary at a different time step and deteriorate the trained model�s accuracy and make them ineffective. However, online machine learning has significant importance to fulfill the demands of the current computing revolution. Moreover, it is essential to understand the existing Concept Drift handling techniques to determine their associated pitfalls and propose robust solutions. This study attempts to summarize and clarify the empirical pieces of evidence of the Concept Drift issue and assess its applicability to meet the current computing revolution. Also, this study provides a few possible research directions and practical implications of Concept Drift handling. © 2020 Authors. This work is licensed under the Creative Commons Attribution-Non- Commercial-NoDerivs 4.0 License https://creativecommons.org/licenses/by-nc-nd/4.0/
format Article
author Hashmani, M.A.
Jameel, S.M.
Rehman, M.
Inoue, A.
spellingShingle Hashmani, M.A.
Jameel, S.M.
Rehman, M.
Inoue, A.
Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
author_sort Hashmani, M.A.
title Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
title_short Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
title_full Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
title_fullStr Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
title_full_unstemmed Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review
title_sort concept drift evolution in machine learning approaches: a systematic literature review
publisher Exeley Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101082701&doi=10.21307%2fijssis-2020-029&partnerID=40&md5=50742a63c76aac5f02af91df2a5ed9ae
http://eprints.utp.edu.my/23350/
_version_ 1741196661749710848
score 11.62408

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