Efficient Concept Drift Detection: A Meta Feature Selection Approach

Zelong Liu, Pingfan Wang, Nanlin Jin*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Concept drift in data streams significantly impacts predictive modelling, as the underlying distribution of the training sample evolves, often leading to increased error rates and degraded model performance. When dealing with big data which has many features and data speed is high, traditional concept drift detection methods might delay in analyzing and detecting in time. This paper introduces an improved drift detection model utilizing multivariate analysis methods for feature selection, thereby enhancing the model’s ability to detect drift more efficiently. Our approach analysis various parameters of the data stream to select the most important features and conduct conduct drift detection based on the selected features. Experimental results demonstrate that this feature-selected drift detection model not only maintains classification performance, but also significantly reducing computational overhead. This predictive framework is particularly valuable in scenarios where large data streams require real-time analysis and where computational resources are limited, providing a practical solution for maintaining robust model performance in dynamic environments.

Original languageEnglish
Title of host publicationICISS 2024 - Proceedings of the 7th International Conference on Information Science and Systems
PublisherAssociation for Computing Machinery, Inc
Pages90-95
Number of pages6
ISBN (Electronic)9798400717567
DOIs
Publication statusPublished - 31 Jan 2025
Event7th International Conference on Information Science and Systems, ICISS 2024 - Edinburgh, United Kingdom
Duration: 14 Aug 202416 Aug 2024

Publication series

NameICISS 2024 - Proceedings of the 7th International Conference on Information Science and Systems

Conference

Conference7th International Conference on Information Science and Systems, ICISS 2024
Country/TerritoryUnited Kingdom
CityEdinburgh
Period14/08/2416/08/24

Keywords

  • Concept Drift Detection
  • Data Stream Mining
  • Feature Selection
  • Meta feature

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