  This paper proposes an autoencoder (AE) that is used for improving the
performance of once-class classifiers for the purpose of detecting anomalies.
Traditional one-class classifiers (OCCs) perform poorly under certain
conditions such as high-dimensionality and sparsity. Also, the size of the
training set plays an important role on the performance of one-class
classifiers. Autoencoders have been widely used for obtaining useful latent
variables from high-dimensional datasets. In the proposed approach, the AE is
capable of deriving meaningful features from high-dimensional datasets while
doing data augmentation at the same time. The augmented data is used for
training the OCC algorithms. The experimental results show that the proposed
approach enhance the performance of OCC algorithms and also outperforms other
well-known approaches.

Machine Learning;Machine Learning