Radial Basis Function Data Descriptor (RBFDD) Network: An Anomaly Detection Approach

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Title: Radial Basis Function Data Descriptor (RBFDD) Network: An Anomaly Detection Approach
Authors: Bazargani, Mehran Hossein ZadehMacNamee, Brian
Permanent link: http://hdl.handle.net/10197/12047
Date: 20-Aug-2018
Online since: 2021-03-12T13:15:16Z
Abstract: In this paper, we propose a modification to the standard Ra-dial Basis Function (RBF) network that transforms it into a one-class classifier suitable for anomaly detection. We name this new approach the Radial Basis Function Data Descriptor (RBFDD) network. The RBFDD network is of interest as it has inherent adaptability in its architecture making it suitable for domains in which concept drift is a concern. Also, features learned by an RBFDD network (i.e., centers and spreads of Gaussian kernels and associated weights) provide us with a level of interpretability that has potential to be quite informative in terms of understanding the model learned and the reasoning behind flagging anomalies. In a set of evaluation experiments we compare the performance of the RBFDD network with some state of the art algorithms for anomaly detection over a collection of benchmark anomaly detection datasets. The results show that the RBFDD network is a promising approach and suggest potential for more investigations and promising directions for future work. We also investigate how RBFDD networks can be interpreted.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Keywords: Machine learning & statisticsAnomaly detectionRadial basis functionNeural networks
Other versions: http://www.andrew.cmu.edu/user/lakoglu/odd/index.html
Language: en
Status of Item: Peer reviewed
Conference Details: ODD v5.0: Outlier Detection De-constructed: Workshop organized in conjunction with ACM SIGKDD, London, UK, 20 August 2018
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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