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.docxТекст статьи: https://towardsdatascience.com/autoencoder-neural-network-for-anomaly-detection-with-unlabeled-dataset-af9051a048
Selvaratnam Lavinan, Autoencoder Neural Network for Anomaly Detection with Unlabled Dataset, Towards Data Science, 2019.
Autoencoder Neural Network for Anomaly Detection with Unlabled Dataset.
The aim of the article is to show the reader how autoencoder neural networks can solve the problem of predicting anomalies in systems with imbalanced data.
The research method is description.
The author describes the idea of autoencoders and how they are structured. Autoencoder tries to reconstruct the input data after it’s compressed and decompressed in hidden layers on the output.
The core logic behind this neural network is to train them to identify normal behaviors of the system, so when anomalies appear this will result in a high mean square error.
The author states that using a combination of several different kinds of neural networks together can yield better results. By setting the threshold of autoencoder low it is possible to detect almost all the anomalies. However, this way some normal states can be flagged as anomalies. Therefore, it is important to reduce the number of false positive result with another neural network.
The article describes the period of 2010s.
The author’s conclusion is that combining several neural networks together can result in a high recall and high precision of the system.