Feature Learning With a Divergence-Encouraging Autoencoder for Imbalanced Data Classification
Imbalanced data exists commonly in machine learning classification applications.Popular Coffee Cups classification algorithms are based on the assumption that data in different classes are roughly equally distributed; however, extremely skewed data, with instances from one class taking up most of the dataset, is not exceptional in practice.Thus, pe