Trading Complexity for Sparsity in Random Forest Explanations

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12371cd2f3c9138f222489cf83d5429e
https://doi.org/10.1609/aaai.v36i5.20484

On the explanatory power of Boolean decision trees

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⟩

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::652490c5e57d8aaa2165bf5f3ffae295
https://hal.science/hal-03950467

On the Computational Intelligibility of Boolean Classifiers

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⟩

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

Statistical Computational Learning

Autor: Frédéric Koriche, Richard Nock, Antoine Cornuéjols

Publikováno v: A Guided Tour of Artificial Intelligence Research ISBN: 9783030061630

Statistical computational learning is the branch of Machine Learning that defines and analyzes the performance of learning algorithms using two metrics: sample complexity and runtime complexity. This chapter is a short introduction to this important

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1b6251c93fb838d9c8b89d25ca2725ef
https://doi.org/10.1007/978-3-030-06164-7_11

Heuristiques de recherche : un bandit pour les gouverner toutes

Autor: Hugues Wattez, Frédéric Koriche, Christophe Lecoutre, Anastasia Paparrizou, Sébastien Tabary

Publikováno v: 15es Journées Francophones de Programmation par Contraintes – JFPC 2019
15es Journées Francophones de Programmation par Contraintes – JFPC 2019, Jun 2019, Albi, France
HAL

International audience; L'automatisation de la configuration des solveurs a reçu une grande attention ces dernières années notam-ment pour la capacité à ajuster ses différents paramètres selon l'instance à résoudre. De plus, cette automatisa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ec5a983c4767c4a8ce2b1b67124ba2a8
https://hal.archives-ouvertes.fr/hal-02414288/document