Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection
This is an incremental improvement for anomaly detection in high-dimensional datasets.
The paper tackles the problem of poor performance in one-class classifiers (OCCs) for anomaly detection under conditions like high-dimensionality and sparsity by using an autoencoder for data augmentation, resulting in enhanced performance that outperforms other approaches.
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.