Hierarchical Approach for Multi-Class Models

This research introduces a novel approach to multi-class classification, harnessing hierarchical architectures to enhance accuracy and interpretability. Specialized models are designed at each hierarchy level, capturing subcategoryspecific nuances using transfer learning. Decision paths are visualized for interpretability. Evaluations across diverse datasets highlight the superior performance of the hierarchical approach in accuracy and scalability. Cross-domain adaptability is demonstrated. This research offers a foundation for hierarchical multi-class classification, revolutionizing complex categorization tasks, and contributing to classification methodology advancements.