A Study on Concept Drift Detection and Adaptation Mechanisms in Real-Time Data Streams
Author(s): Saritha Putta
Publication #: 2507008
Date of Publication: 08.12.2024
Country: India
Pages: 1-5
Published In: Volume 10 Issue 6 December-2024
Abstract
Concept drift poses a significant challenge for predictive models operating in real- time within today’s data-driven systems. Essentially, concept drift refers to the way data distributions can change over time in streaming environments, which can severely affect the accuracy and reliability of predictive models. This paper introduces a flexible architecture designed for real-time detection and adaptation to concept drift, ensuring that model performance remains consistent even as data conditions evolve.
We evaluate various techniques for detecting drift, both supervised and unsupervised, including entropy-based models, representation monitoring using autoencoders, and statistical methods like DDM and EDDM. Furthermore, we propose a hybrid adaptive pipeline that combines ensemble- based model replacement strategies, online learning, and feedback through sliding windows. This work also discusses the trade-offs involved in sensitivity, false alarm rates, and adaptation costs. The findings offer valuable insights for developing robust, self- adaptive machine learning models suitable for real- time applications such as dynamic recommendation systems, IoT sensor monitoring, and fraud detection.
Keywords: Concept Drift, DDM, ADWIN, drift detection, drift adaptation
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