UE 1.7 Ensemble of Classifiers

The course is intended to give the students a fairly comprehensive view of fundamentals and techniques for combining multiple classifiers or machine learning models.

To master the art of combining classifiers.

• UE Apprentissage Automatique du S1
• UE Théories et Algorithmes d’Apprentissage Automatique du S2

Contrôle continu

• Multiple Classifiers Systems (motivation, terminology, applications, taxonomy of fusion methods: sequential, parallel, hybrid architectures)
• Combining/fusion operators (class-based, rank-based, measure based; parametric, non parametric; stacking)
• Ensemble of classifiers (diversity in ensembles, cross-validated committees, bagging, boosting, random subspaces, ECOC, random forests and variants, XGBoost)