OPTIMIZED ADAPTIVE MACHINE LEARNING FOR DYNAMIC DATA STREAMS
Sakhipov A. Mektepbayeva A. Talgat A. Rakhmetov M. Adiyeva A. Seitenov A. Ualiyev N. Yelezhanova S.
2025Technology Center
Eastern-European Journal of Enterprise Technologies
2025#6Issue 315 - 25 pp.
The object of the study is the adaptive machine learning systems that are able to process large amounts of rapidly changing streaming data in real time. The problem of maintaining prediction accuracy and computational efficiency in the presence of concept drift is treated. Concept drift refers to the overweighting of static models when stationary models are tried, and the nature of the underlying distributions changes. The adaptive architecture includes revision divergence-oriented concept drift detection, incremental model updating via hyper-dimensional statistical clustering of segments. Results from experiments using simulated and real-world datasets demonstrate that the adaptive architecture maintains predictive accuracy above 0.83 across abrupt, gradual, recurrent, and continuous drift scenarios. Compared with non-adaptive models, adaptation latency is reduced by approximately 2.6×, while unnecessary retraining operations are decreased by up to 40%. These results are possible due to the fact that proposed framework is able to retrain solutions if, and only if, distributional changes are determined to be statistically significant and meaningful to the model. This leads to the avoidance of processors being given redundant computations and providing a steady-state model during non-drift conditions. A principal contribution is that feature engineering is accomplished in a drift-aware manner, thresholding is made adaptive to the distributions indicated, and update mechanisms are employed which efficiently utilize resources in a unified high-performance streaming pipeline. The architecture performs well under abrupt, gradual, recurrent, and continuous drift and effective for real-time applications which include smart-city analytics, cyber security monitoring, roadways system works, and IoT for industrial systems Copyright
adaptation model , computing performance , drift detection , rapid updating , streaming processing
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Department of Information Technology, K. Kulazhanov Kazakh University of Technology and Business, Kaiym Mukhamedkhanov str., 37A, Astana, 010000, Kazakhstan
Department of Computer Science, I. Zhansugurov Zhetysu University, I. Zhansugurov str., 187a, Taldykorgan, 040009, Kazakhstan
Department of Mathematics and Methodical Тeaching of Mathematics, I. Zhansugurov Zhetysu University, I. Zhansugurov str., 187a, Taldykorgan, 040009, Kazakhstan
Department of Information Technology and Artificial Intelligence, I. Zhansugurov Zhetysu University, I. Zhansugurov str., 187a, Taldykorgan, 040009, Kazakhstan
Department of Software Engineering, School of Software Engineering Astana IT University, Mangilik El ave., 55/11, Astana, 010000, Kazakhstan
Kh. Dosmukhamedov Atyrau University, Studenchesky ave., 1, Atyrau, 060011, Kazakhstan
Department of Information Technology
Department of Computer Science
Department of Mathematics and Methodical Тeaching of Mathematics
Department of Information Technology and Artificial Intelligence
Department of Software Engineering
Kh. Dosmukhamedov Atyrau University
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