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

Title:
Integrating and reporting full multi-view supervised learning experiments using SuMMIT
Authors:
Bauvin, Baptiste, Corbeil, Jacques, Benielli, Dominique, Koço, Sokol, Capponi, Cecile
Contributors:
Université Laval [Québec] (ULaval), éQuipe d'AppRentissage de MArseille (QARMA), Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon) (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche du CHU de Québec-Université Laval (CRCHUQ), CHU de Québec–Université Laval, Université Laval [Québec] (ULaval)-Université Laval [Québec] (ULaval), Aix-Marseille Université - Faculté des Sciences (AMU SCI), Aix Marseille Université (AMU), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)
Source:
Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, Sep 2022, Grenoble & On-line event, France
Publisher Information:
CCSD, 2022.
Publication Year:
2022
Collection:
collection:UNIV-TLN
collection:CNRS
collection:UNIV-AMU
collection:LIS-LAB
collection:INSTITUTS-TELECOM
collection:INCIAM
Subject Terms:
Interpretability, Explainability, Modularity, Reproducible Research, Python, Benchmarking, Classification, Supervised, Multimodal, [INFO.INFO-LG]Computer Science [cs], Machine Learning [cs.LG]
Subject Geographic:
Grenoble & On-line event, France
Original Identifier:
HAL: hal-03845435
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Access URL:
https://hal.science/hal-03845435
https://hal.science/hal-03845435v1/document
https://hal.science/hal-03845435v1/file/bauvin22a.pdf
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.03845435v1
Database:
HAL

Weitere Informationen

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.