146 citations · 220 across the 5 of their papers we have counts for
5 papers
LCE: An Augmented Combination of Bagging and Boosting in Python
Kevin Fauvel, Élisa Fromont, Véronique Masson +2
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LC…
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
Kevin Fauvel, Tao Lin, Véronique Masson +2
Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state…
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
Kevin Fauvel, Véronique Masson, Élisa Fromont
Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics…
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
Kevin Fauvel, Élisa Fromont, Véronique Masson +2
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-…
Mining relevant interval rules
Thomas Guyet, René Quiniou, Véronique Masson
This article extends the method of Garriga et al. for mining relevant rules to numerical attributes by extracting interval-based pattern rules. We propose an algorithm that extract…