Integrating and reporting full multi-view supervised learning experiments using SuMMIT

Baptiste Bauvin (1, 2) , Jacques Corbeil (3) , Dominique Benielli (4) , Sokol Koço (5) , Cecile Capponi (2)

1 ULaval - Université Laval [Québec]
2 QARMA - éQuipe d'AppRentissage de MArseille
3 CRCHUQ - Centre de recherche du CHU de Québec-Université Laval
4 AMU SCI - Aix-Marseille Université - Faculté des Sciences
5 Mines Saint-Étienne MSE - École des Mines de Saint-Étienne

SuMMIT (Supervised Multi Modal Integration Tool) is a software offering many functionalities for running, tuning, and analyzing experiments of supervised classification tasks specifically designed for multi-view data sets. SuMMIT is part of a platform 1 that aggregates multiple tools to deal with multiview datasets such as scikit-multimodallearn (Benielli et al., 2021) or MAGE (Bauvin et al., 2021). This paper presents use cases of SuMMIT, including hyper-parameters optimization, demonstrating the usefulness of such a platform for dealing with the complexity of multi-view benchmarking on an imbalanced dataset. SuMMIT is powered by Python3 and based on scikit-learn, making it easy to use and extend by plugging one's own specific algorithms, score functions or adding new features 2. By using continuous integration, we encourage collaborative development.

Mots clés: Explainability; Interpretability; Multimodal; Supervised; Classification; Benchmarking; Python; Reproducible Research; Modularity

Domaines: Apprentissage [cs.LG]

Baptiste Bauvin, Jacques Corbeil, Dominique Benielli, Sokol Koço, Cecile Capponi. Integrating and reporting full multi-view supervised learning experiments using SuMMIT. 2022. ⟨hal-03845435⟩