The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection

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Title: The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection
Authors: Bazargani, Mehran Hossein ZadehMacNamee, 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 & statisticsAnomaly detectionElliptical basis functionNeural 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

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