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Feature Selection in Marketing Applications

  • Stefan Lessmann
  • Stefan Voß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5678)

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.

Keywords

Marketing Decision Support Classification Feature Selection Support Vector Machines 

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References

  1. 1.
    Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)Google Scholar
  2. 2.
    Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627–635 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
  4. 4.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Friedman, J.H.: Recent advances in predictive (machine) learning. Journal of Classification 23(2), 175–197 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Guyon, I.M., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  8. 8.
    Joachims, T.: Training linear SVMs in linear time. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D. (eds.) Proc. of the 12th ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, pp. 217–226. ACM Press, New York (2006)CrossRefGoogle Scholar
  9. 9.
    Keerthi, S.S., DeCoste, D.: A modified finite newton method for fast solution of large scale linear SVMs. Journal of Machine Learning Research 6, 341–361 (2005)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Kim, Y.S., Street, W.N., Russell, G.J., Menczer, F.: Customer targeting: A neural network approach guided by genetic algorithms. Management Science 51(2), 264–276 (2005)CrossRefGoogle Scholar
  11. 11.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)CrossRefzbMATHGoogle Scholar
  12. 12.
    Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering 34(4), 485–496 (2008)CrossRefGoogle Scholar
  13. 13.
    Lessmann, S., Voß, S.: Supervised classification for decision support in customer relationship management. In: Bortfeldt, A., Homberger, J., Kopfer, H., Pankratz, G., Strangmeier, R. (eds.) Intelligent Decision Support, pp. 231–253. Gabler, Wiesbaden (2008)CrossRefGoogle Scholar
  14. 14.
    Lessmann, S., Voß, S.: A reference model for customer-centric data mining with support vector machines. European Journal of Operational Research 199(2), 520–530 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Martens, D., Baesens, B., van Gestel, T., Vanthienen, J.: Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research 183(3), 1466–1476 (2007)CrossRefzbMATHGoogle Scholar
  16. 16.
    Sindhwani, V., Rakshit, S., Deodhar, D., Erdogmus, D., Principe, J., Niyogi, P.: Feature selection in MLPs and SVMs based on maximum output information. IEEE Transactions on Neural Networks 15(4), 937–948 (2004)CrossRefGoogle Scholar
  17. 17.
    Stoppiglia, H., Dreyfus, G., Dubois, R., Oussar, Y.: Ranking a random feature for variable and feature selection. Journal of Machine Learning Research 3, 1399–1414 (2003)zbMATHGoogle Scholar
  18. 18.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefan Lessmann
    • 1
  • Stefan Voß
    • 1
  1. 1.Institute of Information SystemsUniversity of HamburgHamburgGermany

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