A Voting-Based Ensemble Method for Deep Learning Performance Enhancement

Overfitting and limited generalization remain significant challenges for deep learning models, often leading to suboptimal performance on unseen data. To address this, The Divided Ensemble Voting (DEV) method was introduced, a novel approach that strategically partitions a dataset into distinct sub-sets to train an independent model on each partition. This division encourages each model to specialize in unique features and patterns, thereby increasing ensemble diversity. Predictions from all models are aggregated through a majority voting mechanism to determine the final output, which mitigates overfitting and improves generalization. The proposed method was rigorously evaluated on four binary image classification tasks: Deepfake & Real, Waste Classification, Concrete & Pavement Crack, and Non & Biodegradable Material. Experimental results demonstrate that DEV consistently surpasses the performance of conventional singular models. Accuracy rates improved from 85.55% to 93.1%, 85.12% to 89.6%, 95.42% to 99.0%, and 89.00% to 93.0%, respectively, across the datasets. These findings underscore the efficacy of strategic data partitioning and ensemble consensus in advancing deep learning performance.