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Concept drift presents a formidable hurdle in the implementation of machine learning models in practical scenarios, owing to the potential changes in underlying data distributions over time. The timely detection and effective mitigation of concept drift are imperative for upholding the precision and dependability of predictive models. A promising avenue for detecting concept drift is the Page-Hinkley test, renowned for its adaptability to non-stationary data streams and its sensitivity to gradual alterations. This research endeavors to scrutinize the Page-Hinkley test's efficacy in concept drift detection, delving into its amalgamation with strategies for managing drift, and scrutinizing the attendant challenges and prospects. By leveraging theoretical scrutiny, empirical investigations, and pragmatic insights, this study aims to furnish a comprehensive comprehension of concept drift detection and alleviation utilizing the Page-Hinkley test within machine learning applications.

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Identifying and Managing Concept Drift in Machine Learning Through Page-Hinkley Test: Approaches, Obstacles, and Resolutions

  • 会议论文

  • 发表日期：

    2025年

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  • Navya Francis,
  • Anooja Ali

摘要

Concept drift presents a formidable hurdle in the implementation of machine learning models in practical scenarios, owing to the potential changes in underlying data distributions over time. The timely detection and effective mitigation of concept drift are imperative for upholding the precision and dependability of predictive models. A promising avenue for detecting concept drift is the Page-Hinkley test, renowned for its adaptability to non-stationary data streams and its sensitivity to gradual alterations. This research endeavors to scrutinize the Page-Hinkley test's efficacy in concept drift detection, delving into its amalgamation with strategies for managing drift, and scrutinizing the attendant challenges and prospects. By leveraging theoretical scrutiny, empirical investigations, and pragmatic insights, this study aims to furnish a comprehensive comprehension of concept drift detection and alleviation utilizing the Page-Hinkley test within machine learning applications.