Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability

arXiv:2601.00655v3 Announce Type: replace-cross Abstract: This paper introduces Interpretability-Guided Bi-objective Optimization (IGBO), a framework that trains interpretable models by incorporating structured domain knowledge via a bi-objective formulation. IGBO encodes feature importance hierarchies as a Directed Acyclic Graph (DAG) via Central Limit Theorem-based construction and uses Temporal Integrated Gradients (TIG) to measure feature importance. The framework employs a novel Relative Im