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

Baptiste Bauvin, Jacques Corbeil, Dominique Benielli, Sokol Koço, Cecile Capponi
Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 183:139-150, 2022.

Abstract

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 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. By using continuous integration, we encourage collaborative development.

Cite this Paper

BibTeX
@InProceedings{pmlr-v183-bauvin22a, title = {Integrating and reporting full multi-view supervised learning experiments using SuMMIT}, author = {Bauvin, Baptiste and Corbeil, Jacques and Benielli, Dominique and Ko\c{c}o, Sokol and Capponi, Cecile}, booktitle = {Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {139--150}, year = {2022}, editor = {Moniz, Nuno and Branco, Paula and Torgo, Luís and Japkowicz, Nathalie and Wozniak, Michal and Wang, Shuo}, volume = {183}, series = {Proceedings of Machine Learning Research}, month = {23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v183/bauvin22a/bauvin22a.pdf}, url = {https://proceedings.mlr.press/v183/bauvin22a.html}, abstract = {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 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. By using continuous integration, we encourage collaborative development.} }
Endnote
%0 Conference Paper %T Integrating and reporting full multi-view supervised learning experiments using SuMMIT %A Baptiste Bauvin %A Jacques Corbeil %A Dominique Benielli %A Sokol Koço %A Cecile Capponi %B Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2022 %E Nuno Moniz %E Paula Branco %E Luís Torgo %E Nathalie Japkowicz %E Michal Wozniak %E Shuo Wang %F pmlr-v183-bauvin22a %I PMLR %P 139--150 %U https://proceedings.mlr.press/v183/bauvin22a.html %V 183 %X 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 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. By using continuous integration, we encourage collaborative development.
APA
Bauvin, B., Corbeil, J., Benielli, D., Koço, S. & Capponi, C.. (2022). Integrating and reporting full multi-view supervised learning experiments using SuMMIT. Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 183:139-150 Available from https://proceedings.mlr.press/v183/bauvin22a.html.

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