An Imbalance-Resilient Deep Classification Framework for Reliable Analytics in Real-World Data Streams

Abstract

Data streams in the real world often display severe class imbalance, with a critical under-representation of minority-class instances, resulting in biased and unreliable classification results from conventional deep learning frameworks. We introduce a new Imbalance-Resilient Deep Classification Framework that is defined for robust and stable analytics deployed on dynamic non-stationary data streams. The proposed framework combines adaptive resampling strategies, cost-sensitive learning mechanisms, and deep neural architectures to address the adverse impact of imbalanced class distributions. To cope with this, we propose a new dynamic threshold calibration module which gradually updates decision boundaries according to the changing characteristics of data streams in order to ensure relatively high classification accuracy overtime. The framework also includes an ensemble-driven feature extraction pipeline, enabling to learn discriminative representation for minority classes without compromising majority-class accuracy. The experiments conducted on benchmark and real-world imbalanced streaming datasets show that the proposed approach outperforms existing state-of-the-art algorithms, both in G-Mean, AUC-ROC and F1-Score metrics. Our findings validate the convenience, scalability and broad applicability of our framework in important applications like fraud detection, medical diagnosis, and network intrusion where imbalance constantly arises as a prominent issue.