ROBUST GENERALIZATION AND STRUCTURAL STABILITY IN HIGH-DIMENSIONAL MACHINE LEARNING SYSTEMS WITH IMBALANCED DATA DISTRIBUTIONS

Abstract High-dimensional machine learning systems increasingly operate in environments characterized by extreme feature dimensionality, heterogeneous signal distribution, and pronounced class imbalance. Applications in medical diagnostics, fraud detection, cybersecurity, and industrial monitoring frequently involve sparse minority events embedded within massive majority-class datasets. Under such conditions, conventional training procedures often produce models with deceptively high aggregate accuracy yet poor minority-class sensitivity and unstable generalization behavior. This study develops a structural framework for analyzing robustness and generalization stability in high-dimensional machine learning systems subject to imbalanced data distributions. Emphasis is placed on structural risk behavior, representation stability, sensitivity to distributional shift, and resilience under perturbation of training samples. Rather than focusing exclusively on algorithm-specific optimization, the investigation adopts a system-level perspective integrating statistical theory, computational regularization, and empirical stability validation.