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Feature Selection in Marketing Applications

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Advanced Data Mining and Applications (ADMA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

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Abstract

The paper is concerned with marketing applications of classification analysis. Feature selection (FS) is crucial in this domain to avoid cognitive overload of decision makers through use of excessively large attribute sets. Whereas algorithms for feature ranking have received considerable attention within the literature, a clear strategy how a subset of attributes should be selected once a ranking has been obtained is yet missing. Consequently, three candidate FS procedures are presented and contrasted by means of empirical experimentation on real-world data. The results offer some guidance which approach should be employed in practical applications and identify promising avenues for future research.

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Author information

Authors and Affiliations

  1. Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, D-20146, Hamburg, Germany

    Stefan Lessmann & Stefan Voß

Authors
  1. Stefan Lessmann
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  2. Stefan Voß
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Editor information

Editors and Affiliations

  1. Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Xinjiekouwai Ave. 19, 100875, Beijing, China

    Ronghuai Huang

  2. The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong, Hong Kong

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  4. Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João Gama

  5. School of Information, Zhongguancum, Renmin University, 100872, Beijing, China

    Xiaofeng Meng

  6. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, St. Lucia, Queensland, Australia

    Xue Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Lessmann, S., Voß, S. (2009). Feature Selection in Marketing Applications. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_21

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  • DOI: https://doi.org/10.1007/978-3-642-03348-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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