Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

Our paper, Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression, written with Samuel Stocksieker and Denys Pommeret, has been accepted for publication in TMLR (Transactions on Machine Learning Research)

In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates and especially dedicated to the problem of imbalanced data. This general approach encompasses two large families of synthetic oversampling: those based on perturbations, such as Gaussian Noise, and those based on interpolations, such as SMOTE. It also provides an explicit form of such machine learning algorithms. New synthetic data generators are deduced. We apply GOLIATH in imbalanced regression combining such generator procedures with a new wild-bootstrap resampling technique for the target values. We evaluate the performance of the GOLIATH algorithm in imbalanced regression where we compare our approach with state-of-the-art techniques.

 


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (June 24, 2024). Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory. Freakonometrics. Retrieved July 17, 2024 from https://doi.org/10.58079/11vuo


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.