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Ellipse Support Vector Data Description

  • Conference paper
Engineering Applications of Neural Networks (EANN 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 43))

Abstract

This paper presents a novel Boundary-based approach in one-class classification that is inspired by support vector data description (SVDD). The SVDD is a popular kernel method which tries to fit a hypersphere around the target objects and of course more precise boundary is relied on selecting proper parameters for the kernel functions. Even with a flexible Gaussian kernel function, the SVDD could sometimes generate a loose decision boundary. Here we modify the structure of the SVDD by using a hyperellipse to specify the boundary of the target objects with more precision, in the input space. Due to the usage of a hyperellipse instead of a hypersphere as the decision boundary, we named it "Ellipse Support Vector Data Description" (ESVDD). We show that the ESVDD can generate a tighter data description in the kernel space as well as the input space. Furthermore the proposed algorithm boundaries on the contrary of SVDD boundaries are less influenced by change of the user defined parameters.

This work has been partially supported by Iran Telecommunication Research Center (ITRC), Tehran, Iran. Contract No: T/500/1640.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad (FUM), Mashhad, Iran

    Mohammad GhasemiGol, Reza Monsefi & Hadi Sadoghi Yazdi

Authors
  1. Mohammad GhasemiGol
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  2. Reza Monsefi
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  3. Hadi Sadoghi Yazdi
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Editor information

Editors and Affiliations

  1. Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, N7 8DB, London, UK

    Dominic Palmer-Brown

  2. School of Computing, IT and Engineering, University of East London, Docklands Campus, 4-6 University Way, E16 2RD, London, UK

    Chrisina Draganova  & Haris Mouratidis  & 

  3. School of Computing, IT and Engineering, University of East London, London, UK

    Elias Pimenidis

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© 2009 Springer-Verlag Berlin Heidelberg

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GhasemiGol, M., Monsefi, R., Yazdi, H.S. (2009). Ellipse Support Vector Data Description. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_24

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  • DOI: https://doi.org/10.1007/978-3-642-03969-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03968-3

  • Online ISBN: 978-3-642-03969-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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