most citedOn the Explanatory Power of Decision Trees

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2025

User Profiles of Sleep Disorder Sufferers: Towards Explainable Clustering and Differential Variable Analysis

Sifeddine Sellami, Juba Agoun, Lamia Yessad +1

Sleep disorders have a major impact on patients' health and quality of life, but their diagnosis remains complex due to the diversity of symptoms. Today, technological advances, co…

cs.LG2025

Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI

Chenrui Zhu, Louenas Bounia, Vu Linh Nguyen +2

Recent advancements in machine learning have emphasized the need for transparency in model predictions, particularly as interpretability diminishes when using increasingly complex…

cs.AI2021★ 4 cited

On the Explanatory Power of Decision Trees

Gilles Audemard, Steve Bellart, Louenas Bounia +3

Decision trees have long been recognized as models of choice in sensitive applications where interpretability is of paramount importance. In this paper, we examine the computationa…

cs.AI2021

Trading Complexity for Sparsity in Random Forest Explanations

Gilles Audemard, Steve Bellart, Louenas Bounia +3

Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature…

cs.AI2021

On the Computational Intelligibility of Boolean Classifiers

Gilles Audemard, Steve Bellart, Louenas Bounia +3

In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers und…