Adaptive Learning Framework For Concept Drift Mitigation In Real-Time Data Streams

Authors: Kasu Shashanth Kumar, K.Bharath Kumar, Katiki Bhuvaneswar Sai, Mr.J.Manivannan M.E

Abstract: Machine learning models deployed in real-world environments often operate on continuous data streams where the underlying data distribution evolves over time. This phenomenon, known as concept drift, degrades predictive performance and requires continuous adaptation. Existing approaches primarily focus on drift detection while relying on frequent retraining, which is computationally expensive and impractical for real-time systems. To address this limitation, this paper proposes an adaptive learning framework designed to detect, characterize, and mitigate concept drift in real-time data streams. The proposed framework integrates a drift detection module, a drift memory mechanism to store historical drift patterns, and a selective incremental model update strategy based on drift severity. This approach reduces redundant retraining while maintaining model stability and adaptability. Experimental evaluation demonstrates improved adaptability and computational efficiency compared to traditional retraining-based methods. The proposed framework provides a scalable solution for maintaining machine learning performance in dynamic streaming environments.