Name
Title | ||
|---|---|---|
Extending an Anomaly Detection Benchmark with Auto-encoders, Isolation Forests, and RBMs. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30275-7_39 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Anomaly detection,Sparse auto-encoder,Isolation forest,Restricted Boltzmann machine,Benchmark,U-index | Anomaly detection,Restricted Boltzmann machine,Data set,Boltzmann machine,Computer science,Algorithm,Auto encoders,Outlier | Conference |
Volume | ISSN | Citations |
1078 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Pijnenburg | 1 | 0 | 0.68 |
| Wojtek Kowalczyk | 2 | 0 | 0.34 |