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Master Thesis: Ensemble Methods für Deep Learning based Anomaly Detection
Ensemble Methods für Deep Learning based Anomaly Detection
This work explores a modified bagging approach utilizing deep learning base models: Sampling without replacement, fitting of an autoencoder on each sampled subset and maximum-aggregation of the reconstruction errors of each base model into one anomaly score. The performance of the proposed ensemble method is compared to a range of classical anomaly detection models, single autoencoders as well as to the regular bagging ensemble and evaluated on datasets originating from different domains. Effectiveness of the proposed method was shown for the MNIST dataset, while the ensemble method did not provide any improvements compared to baseline models on the Cifar-10 and HDFS log dataset.
Supervisor: Boris Lorbeer 
Type: Master Thesis
Duration: 6 months
10587 Berlin, Germany
Phone: +49 30 8353 58811
Fax: +49 30 8353 58409