AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling
This work addresses the problem of anomaly detection for applications with scarce labeled data, presenting an incremental improvement by integrating existing techniques in a novel architecture.
The paper tackles the challenge of anomaly detection with limited labeled data by proposing AnoRand, a semi-supervised deep learning method that combines autoencoders and feed-forward perceptrons with random synthetic label generation. The results show that AnoRand generally outperforms 17 state-of-the-art unsupervised methods on synthetic and 57 real-world datasets, achieving the best AUC ROC and AUC PR performance on most datasets, and also performs well in supervised settings.
Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all. In this paper, we present a new semi-supervised anomaly detection method called \textbf{AnoRand} by combining a deep learning architecture with random synthetic label generation. The proposed architecture has two building blocks: (1) a noise detection (ND) block composed of feed forward ferceptron and (2) an autoencoder (AE) block. The main idea of this new architecture is to learn one class (e.g. the majority class in case of anomaly detection) as well as possible by taking advantage of the ability of auto encoders to represent data in a latent space and the ability of Feed Forward Perceptron (FFP) to learn one class when the data is highly imbalanced. First, we create synthetic anomalies by randomly disturbing (add noise) few samples (e.g. 2\%) from the training set. Second, we use the normal and the synthetic samples as input to our model. We compared the performance of the proposed method to 17 state-of-the-art unsupervised anomaly detection method on synthetic datasets and 57 real-world datasets. Our results show that this new method generally outperforms most of the state-of-the-art methods and has the best performance (AUC ROC and AUC PR) on the vast majority of reference datasets. We also tested our method in a supervised way by using the actual labels to train the model. The results show that it has very good performance compared to most of state-of-the-art supervised algorithms.