Unified Predictive Modeling: Enhancing Accuracy with DBNs, Fuzzy ARTMAP, and SVMs

Abstract

Amid the global surge in artificial intelligence, the field of machine learning is advancing rapidly, and selecting the most suitable algorithm for prediction tasks remains a crucial challenge. This paper introduces a novel ensemble model that combines three machine learning algorithms—Deep Belief Networks (DBNs), Fuzzy ARTMAP, and Support Vector Machines (SVMs)—to enhance predictive performance. Each machine learning model possesses unique strengths, and by integrating these models, it is possible to overcome individual model limitations and achieve more accurate and reliable predictions. DBNs excel at learning hierarchical representations and capturing complex patterns, Fuzzy ARTMAP is proficient in handling imprecise and ambiguous data, and SVMs are renowned for their robustness in high-dimensional spaces. Thus, the integrated framework leverages the complementary strengths of each model while mitigating their weaknesses.  In this study, the proposed ensemble model's predictive power was validated through experiments on image data collected from actual construction sites for construction automation research. The prediction performance of the proposed ensemble model was evaluated and compared with that of individual models such as DBNs, Fuzzy ARTMAP, and SVMs, demonstrating its superiority. The experimental results showed that the proposed model outperformed each individual algorithm in terms of prediction accuracy, clearly illustrating the effectiveness of the ensemble approach.