Contribution of machine learning in modeling rare values and imbalanced data

This morning (Montréal time), Samuel Stocksieker defended his PhD thesis entitled “contribution of machine learning in modeling rare values and imbalanced data“. Cécile Capponi, Marianne Clausel, Julie Josse, Frédéric Planchet and  Anne Sabourin, Christian-Yann Robert and Stéphane Loisel were in the jury,

the work is structured around major axes: Imbalanced Features and Imbalanced Regression. The first axis addresses the issue of feature imbalance, that is, when it concerns the attributes and not the variable to be explained. The first solution involves adjusting the distribution of a continuous covariate relative to a given target distribution. It proposes to combine weighted resampling and synthetic data generators. This strategy notably allows to deal with selection bias: when the distribution of the covariate in the training sample is significantly different from that of the population. A second solution is proposed in the context of multi-supervised learning, particularly with autoencoders. It relies on a new metric aimed at balancing the influence of variables during learning and is applicable not only to supervised and unsupervised models, but also to generative models such as variational autoencoders. The second part deals with the issue of regression from imbalanced data. Various preprocessing solutions, including synthetic data generation, are proposed. Initially, we propose to explore the initial data space by introducing new generators and methodologies to address the specific case of regression. We then propose to immerse the data in a latent space in order to provide a more conducive framework for synthetic data generation.


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (June 28, 2024). Contribution of machine learning in modeling rare values and imbalanced data. Freakonometrics. Retrieved July 17, 2024 from https://doi.org/10.58079/11wfo


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