Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection

TL;DR

This paper introduces an autoencoder-based data augmentation method to improve the performance of one-class classifiers in anomaly detection, especially in high-dimensional and sparse data scenarios.

Contribution

The paper presents a novel autoencoder approach that simultaneously performs feature extraction and data augmentation to enhance one-class classifier effectiveness.

Findings

Improved anomaly detection accuracy with the proposed method.

Outperforms existing data augmentation and feature extraction techniques.

Enhances one-class classifier robustness in high-dimensional settings.

Abstract

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.