Autoencoder and deep autoencoder have been widely used for dimensionality reduction and anomaly detection. The ensemble learning method based on autoencoders further improves the accuracy of anomaly detection. However, neural networks are easy to overfit, and the current ensemble methods based on autoencoders cannot effectively make autoencoders diversified to avoid overfitting problems. For this reason, this paper proposes an ensemble method of autoencoders. The algorithm builds a cascaded model of deep autoencoders, and resample the training set of the next neural network by the anomaly detection results of the previous neural network, thereby improving the accuracy of the overall model. Experimental results show that the accuracy of the model is significantly improved compared to the current mainstream anomaly detection algorithms.
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