Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection
In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-T...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/12/786 |
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| author | Gurgen Hovakimyan Jorge Miguel Bravo |
| author_facet | Gurgen Hovakimyan Jorge Miguel Bravo |
| author_sort | Gurgen Hovakimyan |
| collection | DOAJ |
| description | In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-Text Transfer Transformer) to enhance the accuracy and efficiency of screening and data extraction processes. We assess strategies for handling the concept drift in machine learning using high-impact publications from notable databases that were made accessible via the IEEE and Science Direct APIs. The chronological analysis covering the past two decades provides a historical perspective on methodological advancements, recognizing their strengths and weaknesses through citation metrics and rankings. This review aims to trace the growth and evolution of concept drift mitigation strategies and to provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Key findings highlight the effectiveness of diverse methodologies such as drift detection methods, window-based methods, unsupervised statistical methods, and neural network techniques. However, challenges remain, particularly with imbalanced data, computational efficiency, and the application of concept drift detection to non-tabular data like images. This review aims to trace the growth and evolution of concept drift mitigation strategies and provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. |
| format | Article |
| id | doaj-art-351f19b8ba4e4772a5517e0ec7fb9da0 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-351f19b8ba4e4772a5517e0ec7fb9da02025-08-20T02:53:26ZengMDPI AGInformation2078-24892024-12-01151278610.3390/info15120786Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift DetectionGurgen Hovakimyan0Jorge Miguel Bravo1NOVA IMS—Information Management School, New University of Lisbon, 1070-312 Lisbon, PortugalNOVA IMS—Information Management School, New University of Lisbon, 1070-312 Lisbon, PortugalIn this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanced natural language processing model T5 (Text-to-Text Transfer Transformer) to enhance the accuracy and efficiency of screening and data extraction processes. We assess strategies for handling the concept drift in machine learning using high-impact publications from notable databases that were made accessible via the IEEE and Science Direct APIs. The chronological analysis covering the past two decades provides a historical perspective on methodological advancements, recognizing their strengths and weaknesses through citation metrics and rankings. This review aims to trace the growth and evolution of concept drift mitigation strategies and to provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field. Key findings highlight the effectiveness of diverse methodologies such as drift detection methods, window-based methods, unsupervised statistical methods, and neural network techniques. However, challenges remain, particularly with imbalanced data, computational efficiency, and the application of concept drift detection to non-tabular data like images. This review aims to trace the growth and evolution of concept drift mitigation strategies and provide a valuable resource that guides future research and deepens our understanding of this rapidly changing field.https://www.mdpi.com/2078-2489/15/12/786concept driftsystematic reviewmachine learningtypes of concept driftadaptive strategiesScience Direct API |
| spellingShingle | Gurgen Hovakimyan Jorge Miguel Bravo Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection Information concept drift systematic review machine learning types of concept drift adaptive strategies Science Direct API |
| title | Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection |
| title_full | Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection |
| title_fullStr | Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection |
| title_full_unstemmed | Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection |
| title_short | Evolving Strategies in Machine Learning: A Systematic Review of Concept Drift Detection |
| title_sort | evolving strategies in machine learning a systematic review of concept drift detection |
| topic | concept drift systematic review machine learning types of concept drift adaptive strategies Science Direct API |
| url | https://www.mdpi.com/2078-2489/15/12/786 |
| work_keys_str_mv | AT gurgenhovakimyan evolvingstrategiesinmachinelearningasystematicreviewofconceptdriftdetection AT jorgemiguelbravo evolvingstrategiesinmachinelearningasystematicreviewofconceptdriftdetection |