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Fuzzy–Evolutionary Modeling of Customer Behavior for Business Intelligence

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Marketing Intelligent Systems Using Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 258))

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Abstract

This chapter describes the application of evolutionary algorithms to induce predictive models of customer behavior in a business environment. Predictive models are expressed as fuzzy rule bases, which have the interesting property of being easy to interpret for a human expert, while providing satisfactory accuracy. The details of an island-based distributed evolutionary algorithm for fuzzy model induction are presented and a case study is used to illustrate the effectiveness of the approach.

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Author information

Authors and Affiliations

  1. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, via Bramante 65, I-26013, Crema, Italy

    Célia da Costa Pereira & Andrea G. B. Tettamanzi

Authors
  1. Célia da Costa Pereira
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  2. Andrea G. B. Tettamanzi
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Editor information

Editors and Affiliations

  1. University of Granada, Spain

    Jorge Casillas  & Francisco J. Martínez-López  & 

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da Costa Pereira, C., Tettamanzi, A.G.B. (2010). Fuzzy–Evolutionary Modeling of Customer Behavior for Business Intelligence. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_15

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  • DOI: https://doi.org/10.1007/978-3-642-15606-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15605-2

  • Online ISBN: 978-3-642-15606-9

  • eBook Packages: EngineeringEngineering (R0)

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