Examining Performance of Naïve Bayes and Support Vector Machine for Solid Waste Classification in Automated Sorting Systems

The growing volume of global waste poses significant challenges to effective waste management, underscoring the need for innovative classification methods to improve recycling efficiency. This study evaluates the performance of two traditional machine learning models, Naïve Bayes and Support Vector Machines (SVMs), for classifying solid waste materials in an automated sorting system. A dataset of 284 JPEG images, categorized into five classes (cardboard, glass, metal, paper, and plastic), was utilized. Preprocessing involved resizing images to 512x384 pixels, normalizing pixel values, and extracting features using Histograms of Oriented Gradients (HOG) and Color Histograms. Naïve Bayes demonstrated computational efficiency with 98.90% accuracy and an F1-score of 0.908, but struggled with overlapping features, leading to misclassifications, particularly between glass and metal. In contrast, SVM outperformed Naïve Bayes, achieving 99.80% accuracy and an F1-score of 0.965 by effectively handling complex, overlapping features via optimal decision boundaries. The findings highlight SVM’s superior performance for complex datasets, while Naïve Bayes remains a viable option for simpler tasks. This study underscores the potential of traditional machine learning in waste classification. However, it suggests that integrating deep learning models could improve accuracy, scalability, and adaptability in real-world waste-sorting systems.