Interpretable Multi-Modal Fusion Network for Complex Heterogeneous Data: A Deep Learning Approach with Enhanced Performance and Transparency

Abstract

This study proposes an interpretable multi-modal fusion network designed to handle complex heterogeneous data while maintaining transparency in decision-making. The increasing complexity of real-world data, which often comprises diverse modalities such as text, images, numerical values, and categorical attributes, necessitates models capable of integrating these inputs effectively without sacrificing interpretability. Our approach introduces a hierarchical architecture with specialized sub-layers for processing each data type, followed by a fusion layer that combines features through concatenation and attention mechanisms. The model further incorporates an interpretability layer to elucidate feature importance and decision rules, employing techniques such as SHAP values and rule extraction. This design not only improves performance by dynamically weighting modalities but also provides actionable insights into the model’s predictions. Experiments demonstrate that the proposed method achieves superior accuracy compared to existing approaches while offering clear explanations for its outputs. The framework addresses a critical gap in deep learning by balancing performance with transparency, making it suitable for high-stakes applications where understanding model behavior is essential. Moreover, the modular design allows for flexibility in adapting to various data types and domains, ensuring broad applicability. By integrating advanced fusion strategies with interpretability tools, our work advances the field of multi-modal learning and sets a new standard for transparent AI systems.