Maaroof N; Moreno A; Jabreel M; Valls A (2022). Fuzzy-LORE: A Method for Extracting Local and Counterfactual Explanations Using Fuzzy Decision Trees. Amsterdam: IOS Press
Papper original source:
Frontiers In Artificial Intelligence And Applications. 356 345-354
Abstract:
Classification systems based Machine Learning hide the logic of their internal decision processes from the users. Hence, post-hoc explanations about their predictions are often required. This paper proposes Fuzzy-LORE, a method that generates local explanations for fuzzy-based Machine Learning systems. First, it learns a local fuzzy decision tree using a set of synthetic neighbours from the input instance. Then, it extracts from the logic of the fuzzy decision tree a meaningful explanation consisting of a set of decision rules (which explain the reasons behind the decision), a set of counterfactual rules (which inform of small changes in the instance's features that would lead to a different outcome), and finally a set of specific counterfactual examples. Our experiments on a real-world medical dataset show that Fuzzy-LORE outperforms prior approaches and methods for generating local explanations.
Classification systems based Machine Learning hide the logic of their internal decision processes from the users. Hence, post-hoc explanations about their predictions are often required. This paper proposes Fuzzy-LORE, a method that generates local explanations for fuzzy-based Machine Learning systems. First, it learns a local fuzzy decision tree using a set of synthetic neighbours from the input instance. Then, it extracts from the logic of the fuzzy decision tree a meaningful explanation consisting of a set of decision rules (which explain the reasons behind the decision), a set of counterfactual rules (which inform of small changes in the instance's features that would lead to a different outcome), and finally a set of specific counterfactual examples. Our experiments on a real-world medical dataset show that Fuzzy-LORE outperforms prior approaches and methods for generating local explanations.
Type:
Proceedings Paper info:eu-repo/semantics/publishedVersion
Contributor:
Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili
Artificial Intelligence Diabetic retinopathy Explainable ai (xai) Fuzzy decision tree Lore Machine learning Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii