Automated interpretation using weights of IEINN after learning

Abstract

In this study, we propose an index for determining the contribution of factors and the strength of interactions between factors, based on fuzzy measures and the Shapley Interaction Index. These measures are derived from the weights obtained after training an explainable neural network model, the Inclusion-Exclusion Integral Neural Network (IEINN), which utilizes fuzzy measures as non-additive measures. Additionally, we propose an automated visualization representation of this index.