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Abstract
In this paper, the recently published benchmark of Goldstein and Uchida [3] for unsupervised anomaly detection is extended with three anomaly detection techniques: Sparse Auto-Encoders, Isolation Forests, and Restricted Boltzmann Machines. The underlying mechanisms of these algorithms differ substantially from the more traditional anomaly detection algorithms, currently present in the benchmark. Results show that in three of the ten data sets, the new algorithms surpass the present collection of 19 algorithms. Moreover, a relation is noted between the nature of the outliers in a data set and the performance of specific (clusters of) anomaly detection algorithms.
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Authors and Affiliations
Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands
Mark Pijnenburg & Wojtek Kowalczyk
Netherlands Tax and Customs Administration, Utrecht, The Netherlands
Mark Pijnenburg
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Correspondence toMark Pijnenburg.
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Kaunas University of Technology, Kaunas, Lithuania
Robertas Damaševičius
Kaunas University of Technology, Kaunas, Lithuania
Giedrė Vasiljevienė
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Pijnenburg, M., Kowalczyk, W. (2019). Extending an Anomaly Detection Benchmark with Auto-encoders, Isolation Forests, and RBMs. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-30275-7_39
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