Evolutionary optimisation of RBF network architectures in a direct marketing application

Authors:
Peter Neumann;Bernhard Sick;Dirk Arndt;Wendy Gersten
Affiliations:
University of Passau, Passau;University of Passau, Passau;DaimlerChrysler AG, Research & Technology, Information Mining, Ulm;DaimlerChrysler AG, Research & Technology, Information Mining, Ulm
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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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.