557 research outputs found
Local Universal Rule-based Explanations
Explainable artificial intelligence (XAI) is one of the most intensively
developed are of AI in recent years. It is also one of the most fragmented one
with multiple methods that focus on different aspects of explanations. This
makes difficult to obtain the full spectrum of explanation at once in a compact
and consistent way. To address this issue, we present Local Universal Explainer
(LUX) that is a rule-based explainer which can generate factual, counterfactual
and visual explanations. It is based on a modified version of decision tree
algorithms that allows for oblique splits and integration with feature
importance XAI methods such as SHAP or LIME. It does not use data generation in
opposite to other algorithms, but is focused on selecting local concepts in a
form of high-density clusters of real data that have the highest impact on
forming the decision boundary of the explained model. We tested our method on
real and synthetic datasets and compared it with state-of-the-art rule-based
explainers such as LORE, EXPLAN and Anchor. Our method outperforms currently
existing approaches in terms of simplicity, global fidelity and
representativeness
Wild boar population at the Vistula Spit – management of the species in forested and urban areas
Bobek, B., Frąckowiak, W., Furtek, J., Merta, D., Orłowska, L
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