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Latent sector errors (LSEs) in disk drives cause significant outages, data loss, and unreliability in large-scale cloud storage systems. Predicting LSEs can help avoid these problems and improve cloud reliability. Ensemble classifiers typically outperform individual classifiers for LSE prediction with high accuracy but can lead to underfitting and incurring additional computational cost, complexity, and time and memory consumption. This research addresses this challenge by proposing a twofold solution: optimizing the ensemble diversity of the resulting Random Forest (RF) classifier through accuracy sliding window-based ensemble pruning (SWEP-RF) and using this pruned ensemble to predict LSEs in cloud storage. SWEP-RF maximizes its lower margin distribution to adapt the RF prediction performance and produce a strong-performing and effective subensemble. Our approach also reduces ensemble size while …