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pro vyhledávání: '"Steve Bellart"'
Autor:
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Publikováno v:
HAL
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 subsampling, the resulting random forest can lead to more stable and r
Autor:
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Publikováno v:
Data and Knowledge Engineering
Data and Knowledge Engineering, 2022, 142, pp.102088. ⟨10.1016/j.datak.2022.102088⟩
Data and Knowledge Engineering, 2022, 142, pp.102088. ⟨10.1016/j.datak.2022.102088⟩
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::652490c5e57d8aaa2165bf5f3ffae295
https://hal.science/hal-03950467
https://hal.science/hal-03950467
Autor:
Gilles Audemard, Steve Bellart, Louenas Bounia, Frederic Koriche, Jean-Marie Lagniez, Pierre Marquis
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Abductive explanations take a central place in eXplainable Artificial Intelligence (XAI) by clarifying with few features the way data instances are classified. However, instances may have exponentially many minimum-size abductive explanations, and th
Autor:
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Publikováno v:
HAL
Les forêts aléatoires constituent un modèle d'apprentissage automatique efficace, ce qui explique qu'elles soient encore massivement utilisées aujourd'hui. S'il est assez facile de comprendre le fonctionnement d'un arbre de décision, il est beau
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3f5cf6a1d70a5979e4e936d801adcb6a
https://hal.archives-ouvertes.fr/hal-03699537
https://hal.archives-ouvertes.fr/hal-03699537
Autor:
Louenas Bounia, Jean-Marie Lagniez, Pierre Marquis, Steve Bellart, Frédéric Koriche, Gilles Audemard
Publikováno v:
18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}
18th International Conference on Principles of Knowledge Representation and Reasoning, Nov 2020, Hanoii, France. pp.74-86, ⟨10.24963/kr.2021/8⟩
KR
18th International Conference on Principles of Knowledge Representation and Reasoning, Nov 2021, Hanoii, France. pp.74-86, ⟨10.24963/kr.2021/8⟩
18th International Conference on Principles of Knowledge Representation and Reasoning, Nov 2020, Hanoii, France. pp.74-86, ⟨10.24963/kr.2021/8⟩
KR
18th International Conference on Principles of Knowledge Representation and Reasoning, Nov 2021, Hanoii, France. pp.74-86, ⟨10.24963/kr.2021/8⟩
In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers under consideration are decision trees, DNF formulae, decision lists, dec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6065a7b780061c0abd1cb88a35eed3c9
https://hal.archives-ouvertes.fr/hal-03500007
https://hal.archives-ouvertes.fr/hal-03500007
Autor:
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Publikováno v:
HAL
Decision trees are a learning model suitable for applications where the interpretability of decisions is of paramount importance. Here we examine the ability of binary decision trees to extract, minimize, and count abductive explanations and contrast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::857785196cfb86f97a5d377c2e2b7242
https://hal.archives-ouvertes.fr/hal-03699542
https://hal.archives-ouvertes.fr/hal-03699542