XAI-based cross-ensemble feature ranking methodology for machine learning models
Pei Jiang, H. Suzuki, Takashi Obi
Abstract
Abstract Artificial Intelligence (AI) as one robust technology has been used in various fields, making innovative society possible and changing our lifestyles. However, the black box problem is still one big problem for artificial intelligence. In this study, we first compared the results of kernel Shapley Additive exPlanations (SHAP) for various machine learning models and found that the single SHAP model cannot explain the models at the human knowledge level. Then the factors’ global ranking was calculated using our proposed ensemble methodology. Finally, the new factors’ ranking was compared with other factor ranking method. Our experimental results declare that the proposed cross-ensemble feature ranking methodology provides stable and comparatively reliable feature ranking in both the classification and regression models.