Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets

Oversampling approaches are a popular choice to improve classification on imbalanced datasets. The SMOTE algorithm is the pioneer for many algorithms, built as extensions of SMOTE, to solve its problem of over-generalization of the minority class. Some extensions adopt the approach of learning the minority class data distribution through clustering and manifold learning techniques. The Localised Random Affine Shadowsampling (LoRAS) algorithm, models the convex space, controlling the local variance of a synthetic sample by constructing them from convex combinations of multiple shadow samples generated by adding Gaussian noise to the original minority samples. LoRAS also uses t-SNE for a manifold learning step to identify minority class data neighbourhoods. The algorithm is known to outperform some early SMOTE extensions, improving F1-Score and Balanced accuracy for highly imbalanced classification problems. However, the state-of-the-art manifold learning algorithm UMAP is known to preserve the local and global structure of the latent data manifold better than t-SNE and is considerably faster. We have integrated the UMAP for manifold learning with localized affine shadowsampling, to build the LoRAS-UMAP algorithm. We have benchmarked the new algorithm LoRAS-UMAP against some state-of-the-art oversampling algorithms on 14 publicly available datasets characterized by high imbalance, high dimensionality, and high absolute imbalance. In summary, we incorporated UMAP for the manifold learning step yielding better F1-Score, Balanced accuracy and runtime for the LoRAS algorithm in comparison to t-SNE for manifold learning, particularly in the case of high-dimensional datasets.