A Scalable Machine Learning Framework for Real-Time Data Stream Classification in IoT Environments
Keywords:
Machine Learning, Internet of Things (IoT), Data Stream Processing, Real-Time Classification, Scalable Systems, Online Learning, Concept DriftAbstract
The growth of Internet of Things (IoT) devices generates continuous data streams that require efficient real-time processing. Traditional batch learning methods are not suitable for such dynamic and high-velocity environments. This paper proposes a scalable machine learning framework for real-time data stream classification in IoT systems. The proposed framework is designed to handle large-scale streaming data, adapt to concept drift, and ensure low-latency predictions. It integrates an online learning approach to continuously update the model based on incoming data. Experimental results demonstrate that the proposed framework achieves improved classification accuracy, scalability, and processing efficiency compared to conventional methods.
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