no code implementations • 19 Dec 2023 • Gwladys Kelodjou, Laurence Rozé, Véronique Masson, Luis Galárraga, Romaric Gaudel, Maurice Tchuente, Alexandre Termier
Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework.
1 code implementation • 14 Aug 2023 • Kevin Fauvel, Élisa Fromont, Véronique Masson, Philippe Faverdin, Alexandre Termier
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression.
2 code implementations • 10 Sep 2020 • Kevin Fauvel, Tao Lin, Véronique Masson, Élisa Fromont, Alexandre Termier
Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability.
no code implementations • 29 May 2020 • Kevin Fauvel, Véronique Masson, Élisa Fromont
Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods.
1 code implementation • 7 May 2020 • Kevin Fauvel, Élisa Fromont, Véronique Masson, Philippe Faverdin, Alexandre Termier
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification.
no code implementations • 11 Sep 2017 • 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.