Integrating and Reporting Full Multi-View Supervised Learning Experiments Using SuMMIT.

SuMMIT ( Su pervised M ulti M odal I ntegration T ool) 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.