742 citations · 806 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Mohak Shah, Mario Marchand, Jacques Corbeil
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance…