The selected features are then classified using a Support Vector Machine, effectively combining deep learning’s feature extraction capabilities with machine learning’s robust classification strengths.

The empirical analysis shows that the autoencoder is able to represent problem features in a limited latent space efficiently, as well as convey more information than current feature extraction ...