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Evolutionary Optimisation of RBF Network Architectures in a Direct Marketing Application

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Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

Feature selection and structure optimisation are two key tasks in many neural network applications. This article sets out an evolutionary algorithm (EA) that performs the two tasks simultaneously for radial basis function (RBF) networks. The algorithm selects appropriate input features from a given set of possible features and adapts the number of basis functions (hidden neurons). The feasibility and the benefits of this approach are demonstrated in a direct marketing application in the automotive industry: the selection of promising targets for a direct mailing campaign, i.e. people who are likely to buy a certain product (here: car of a certain make). The method is independent from the application example given so that the ideas and solutions may easily be transferred to other neural network paradigms.

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Author information

Authors and Affiliations

  1. Chair of Computer Architectures (Prof. Dr.-Ing. W. Grass), University of Passau, Innstrasse 33, D-94032, Passau

    Peter Neumann & Bernhard Sick

  2. DaimlerChrysler AG, Research & Technology, Information Mining (RIC/AM), PO Box 2360, D-89013, Ulm

    Dirk Arndt & Wendy Gersten

Authors
  1. Peter Neumann
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  2. Bernhard Sick
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  3. Dirk Arndt
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  4. Wendy Gersten
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Neumann, P., Sick, B., Arndt, D., Gersten, W. (2003). Evolutionary Optimisation of RBF Network Architectures in a Direct Marketing Application. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_37

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  • DOI: https://doi.org/10.1007/3-540-44989-2_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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