The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection
Files in This Item:
|insight_publication.pdf||718.22 kB||Adobe PDF||Request a copy|
|Title:||The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection||Authors:||Bazargani, Mehran Hossein Zadeh; MacNamee, Brian||Permanent link:||http://hdl.handle.net/10197/12009||Date:||30-Apr-2020||Online since:||2021-03-04T16:57:30Z||Abstract:||This paper introduces the Elliptical Basis Function Data Descriptor (EBFDD) network, a one-class classification approach to anomaly detection based on Radial Basis Function (RBF) neural networks. The EBFDD network uses elliptical basis functions, which allows it to learn sophisticated decision boundaries while retaining the advantages of a shallow network. We have proposed a novel cost function, whose minimisation results in a trained anomaly detector that only requires examples of the normal class at training time. The paper includes a large benchmark experiment that evaluates the performance of EBFDD network and compares it to state of the art one-class classification algorithms including the One-Class Support Vector Machine and the Isolation Forest. The experiments show that, overall, the EBFDD network outperforms the state of the art approaches.||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Conference Publication||Publisher:||Springer||Series/Report no.:||Lecture Notes in Computer Science; 11906||Copyright (published version):||2020 Springer||Keywords:||Machine learning & statistics; Anomaly detection; Elliptical basis function; Neural networks||DOI:||10.1007/978-3-030-46150-8_7||Language:||en||Status of Item:||Peer reviewed||Is part of:||Bresfeld, U., Formont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Wurzburg, Germany, September 16-20, 2019, Proceedings, Part 1||Conference Details:||The 2019 Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019), Wurzburg, Germany, 16-20 September 2019||ISBN:||978-3-030-46150-8||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
Insight Research Collection
Show full item record
If you are a publisher or author and have copyright concerns for any item, please email firstname.lastname@example.org and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.