146 citations · 216 across the 8 of their papers we have counts for
10 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…
Generating robust counterfactual explanations
Victor Guyomard, Françoise Fessant, Thomas Guyet +2
Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have…
Persistence-Based Discretization for Learning Discrete Event Systems from Time Series
Lénaïg Cornanguer, Christine Largouët, Laurence Rozé +1
To get a good understanding of a dynamical system, it is convenient to have an interpretable and versatile model of it. Timed discrete event systems are a kind of model that respon…
VCNet: A self-explaining model for realistic counterfactual generation
Victor Guyomard, Françoise Fessant, Thomas Guyet +2
Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modif…
Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
Erwan Bourrand, Luis Galárraga, Esther Galbrun +2
We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful se…
HiPaR: Hierarchical Pattern-aided Regression
Luis Galárraga, Olivier Pelgrin, Alexandre Termier
We introduce HiPaR, a novel pattern-aided regression method for tabular data containing both categorical and numerical attributes. HiPaR mines hybrid rules of the form $p \Rightarr…