no code implementations • 31 Jul 2023 • Vincent Lemaire, Fabrice Clérot, Marc Boullé
In the case of the naive Bayes classifier, and to our knowledge, there is no ``analytical" formulation of Shapley values.
no code implementations • 9 Jun 2023 • Marc Boullé
Histograms are among the most popular methods used in exploratory analysis to summarize univariate distributions.
no code implementations • 27 Dec 2022 • Valentina Zelaya Mendizábal, Marc Boullé, Fabrice Rossi
G-Enum histograms are a new fast and fully automated method for irregular histogram construction.
no code implementations • 22 Dec 2022 • Aichetou Bouchareb, Marc Boullé, Fabrice Clérot, Fabrice Rossi
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks.
no code implementations • 22 Dec 2022 • Aichetou Bouchareb, Marc Boullé, Fabrice Clérot, Fabrice Rossi
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix.
no code implementations • 15 Mar 2021 • Dominique Gay, Alexis Bondu, Vincent Lemaire, Marc Boullé
Supervised learning of time series data has been extensively studied for the case of a categorical target variable.
no code implementations • Springer Cham 2019 • Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michèle Sebag, Alexander Statnikov, WeiWei Tu, Evelyne Viegas
The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn.
Ranked #1 on AutoML on Chalearn-AutoML-1
no code implementations • 6 Feb 2019 • Aichetou Bouchareb, Marc Boullé, Fabrice Rossi, Fabrice Clérot
The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function.
no code implementations • 29 Aug 2016 • Romain Guigourès, Marc Boullé, Fabrice Rossi
This paper introduces a novel technique to track structures in time varying graphs.
no code implementations • 4 Nov 2015 • Mohamed Khalil El Mahrsi, Romain Guigourès, Fabrice Rossi, Marc Boullé
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities.
no code implementations • 30 Oct 2015 • Romain Guigourès, Marc Boullé, Fabrice Rossi
In this article, we introduce a practical analysis of a large database from a telecommunication operator.
no code implementations • 6 Aug 2015 • Marc Boullé
In this paper, we present a novel way to summarize the structure of large graphs, based on non-parametric estimation of edge density in directed multigraphs.
no code implementations • 6 May 2015 • Dominique Gay, Romain Guigourès, Marc Boullé, Fabrice Clérot
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models.
no code implementations • 20 Mar 2015 • Romain Guigourès, Dominique Gay, Marc Boullé, Fabrice Clérot, Fabrice Rossi
Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination, date and time of calls.
no code implementations • 2 Jul 2014 • Marc Boullé, Romain Guigourès, Fabrice Rossi
In this paper, we deal with the problem of curves clustering.