Publication detail

Feature space reduction as data preprocessing for the anomaly detection

BILÍK, Š.

Original Title

Feature space reduction as data preprocessing for the anomaly detection

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

In this paper, we present two pipelines in order to reduce the feature space for anomaly detection using the One Class SVM. As a first stage of both pipelines, we compare the performance of three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipeline and the reconstruction errors based method as the second. Both methods have potential for the anomaly detection, but the reconstruction error metrics prove to be more robust for this task. We show that the convolutional autoencoder architecture doesn't have a significant effect for this task and we prove the potential of our approach on the real world dataset.

Keywords

Anomaly detection;Convolutional autoencoder;PCA;t-SNE;CNN;OC-SVM

Authors

BILÍK, Š.

Released

30. 4. 2021

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-5942-7

Book

Proceedings I of the 27th Conference STUDENT EEICT 2021

Pages from

415

Pages to

419

Pages count

5

URL

https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf

BibTex

@inproceedings{BUT171163,
  author="Šimon {Bilík}",
  title="Feature space reduction as data preprocessing for the anomaly detection",
  booktitle="Proceedings I of the 27th Conference STUDENT EEICT 2021",
  year="2021",
  pages="415--419",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-5942-7",
  url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf"
}