CFIRE: A General Method for Combining Local Explanations
Journal:
arXiv
Published Date:
Apr 1, 2025
Abstract
We propose a novel eXplainable AI algorithm to compute faithful,
easy-to-understand, and complete global decision rules from local explanations
for tabular data by combining XAI methods with closed frequent itemset mining.
Our method can be used with any local explainer that indicates which dimensions
are important for a given sample for a given black-box decision. This property
allows our algorithm to choose among different local explainers, addressing the
disagreement problem, \ie the observation that no single explanation method
consistently outperforms others across models and datasets. Unlike usual
experimental methodology, our evaluation also accounts for the Rashomon effect
in model explainability. To this end, we demonstrate the robustness of our
approach in finding suitable rules for nearly all of the 700 black-box models
we considered across 14 benchmark datasets. The results also show that our
method exhibits improved runtime, high precision and F1-score while generating
compact and complete rules.
