Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification

A model designed for automatic prediction of diseases based on multimedia data collected in hospitals is introduced in this working notes paper. In order to perform the automatic diseases prediction efficiently, while using as few data as possible for training, we develop a two-stage learning strategy, which first performs the weighted discriminant embedding (WDE) to project the original data to a low-dimensional feature subspace and then utilizes the cost-sensitive nearest neighbor (CS-NN) method in the learned subspace for disease prediction. The proposed approach is evaluated on the MediaEval 2018 Medico Multimedia Task. Copyright held by the owner/author(s).