Deep Ensemble Learning Model for Waste Classification Systems

Waste classification is a critical aspect of sustainable waste management systems. Traditional methods for waste classification are often inadequate to handle the complexity and diversity of materials encountered in real-world scenarios. This paper proposes novel deep ensemble learning models that combine pre-trained models with ensemble methods to improve waste classification performance. The proposed model leverages transfer and ensemble learning techniques, employing both averaging and weighted averaging methods to enhance waste classification accuracy. The proposed model is evaluated comprehensively on four publicly available waste image datasets containing various waste categories: TrashNet, TrashBox, Waste Pictures and Garbage Classification. The obtained results show that the averaging and weighted averaging ensemble methods improved classification accuracy by 1% to 3% over the strongest individual models. The weighted ensemble method achieves 96% accuracy, 94% precision, 97% recall and 95% F1 score on the TrashNet dataset. Statistical significance is verified using 5-fold cross-validation and paired t-tests (p < 0.05). To ensure model explainability, the localization of important object regions is demonstrated with Grad-CAM visualizations. Overall, this study validates the potential of integrating deep image classification models with ensemble methodologies to improve the accuracy and efficiency of waste classification. The main contributions of this study can be summarized as follows: we design an efficient deep ensemble method that leverages multiple pre-trained models and ensemble techniques; we employ averaging and weighted averaging techniques to improve classification accuracy and model robustness; and lastly, we evaluate the model using multiple datasets to demonstrate generalizability, scalability and robustness.