Proposal of self and semi-supervised learning for imbalanced classification of coronary heart disease tabular data

Triple Mixup is an augmentation policy in the hidden latent space we introduced in the Contrastive Mixup Self-Semi Supervised learning framework, to address the imbalanced data problem, for Cardiovascular Heart Diseases tabular dataset. Medical tabular datasets are known to present challenges as high imbalanced class, limited annotated quality samples due to the domain nature. Recent literature in Self and Semi supervised learning, has shown tremendous progress in learning useful representations, and leveraging unlabeled dataset and labeled dataset to train a learning model. Most existing methods are not feasible for tabular data due to the data augmentation scheme. In addition, the high imbalanced problem can show lower performance on machine learning algorithms. For this work, we propose the triple data augmentation method in hidden space to attack the unbalanced challenge in self-supervised and semi-supervised learning, from the possible applications of Contrastive Mixup, thus we will study the influence of it.