MMCIE: Multi-order Multivariate Coalition Internal-External Interaction for Image Explanation

Shapley values are extensively utilized in Explainable Artificial Intelligence to interpret predictions made by complex machine learning models. However, much of the research has focused on addressing the costly computational aspects of Shapley values, neglecting the rich interactive knowledge representation inherent in the data within the model. Therefore, MMCIE is proposed in this article, focusing on exploring the conceptual modeling of data by the model. Based on Shapley values, it defines the interaction between inner and outer coalitions to accurately quantify the concept of model modeling. Specifically, the Superpixel Coalitions Selecting Module (SCSM) is proposed, which defines coalitions as entities and paves the way for calculating multivariable interaction values and internal interaction values. Additionally, the Internal and External Interaction Module (IEIM) acts as a bridge, computing and connecting the internal and external interaction values of coalitions, thereby constructing the feature prototype of the model. Moreover, the Multi-order Equidistance Coalitions Modeling Module (MECMM) is introduced as an effective approach to reduce computational complexity and explore the storage methods of the model’s conceptual modeling. Experiments on two datasets show the advantage of MMCIE over existing methods, revealing the model’s concept modeling process through multi-order interactions and offering a fresh view for model interpretation.