Deep learning has achieved significant success in handling unstructured data but remains limited by its "black box" nature, especially in sensitive applications. Existing interpretable machine learning methods partially address this issue but often overlook feature correlations and provide inadequate assessments of model decision paths. To tackle these challenges, this paper introduces Real Explainer (RealExp), a novel interpretability method that decouples the Shapley Value into individual feature importance and feature correlation importance. By integrating feature similarity computations, RealExp enhances interpretability by precisely quantifying both individual contributions and interactions, leading to more reliable explanations. Furthermore, a new interpretability evaluation criterion is proposed, focusing on decision path analysis beyond accuracy-based assessments. Experiments on image classification and sentiment analysis demonstrate RealExp's superiority in interpretability. Case studies highlight its practical benefits: RealExp aids in selecting pre-trained models for pneumonia detection, emphasizes pre-trained video segmentation for anomaly detection, and optimizes text models, achieving performance to RoBERTA finetuning model without pre-trained embeddings.
Relation:
Information Processing & Management 62(4) pp. 104153-104183,
