8 citations · 11 across the 4 of their papers we have counts for
4 papers
Invariant Causal Set Covering Machines
Thibaud Godon, Baptiste Bauvin, Pascal Germain +2
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable…
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin +3
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges l…
Large scale modeling of antimicrobial resistance with interpretable classifiers
Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre +3
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome…
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe +3
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole gen…