Skip to main content
SpringerLink
Log in

Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application

  • Conference paper
Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining (ICDM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4065))

Included in the following conference series:

Abstract

Data mining applications addressing classification problems must master two key tasks: feature selection and model selection. This paper proposes a random feature selection procedure integrated within the multinomial logit (MNL) classifier to perform both tasks simultaneously. We assess the potential of the random feature selection procedure (exploiting randomness) as compared to an expert feature selection method (exploiting domain-knowledge) on a CRM cross-sell application. The results show great promise as the predictive accuracy of the integrated random feature selection in the MNL algorithm is substantially higher than that of the expert feature selection method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, D., Schorling, C.: Market Share Forecasting: An Empirical Comparison of Artificial Neural Networks and Multinomial Logit Model. Journal of Retailing 72(4), 383–407 (1996)

    Article  Google Scholar 

  2. Baltas, G., Doyle, P.: Random utility models in marketing: a survey. Journal of Business Research 51(2), 115–125 (2001)

    Article  Google Scholar 

  3. Barandela, R., Sánchez, J.S., Garcia, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36(3), 849–851 (2003)

    Article  Google Scholar 

  4. Barsalou, L.W.: Deriving Categories to Achieve Goals. In: Bower, G.H. (ed.) The Psychology of Learning and Motivation, pp. 1–64. Academic Press, New York (1991)

    Google Scholar 

  5. Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. The MIT Press, Cambridge (1985)

    Google Scholar 

  6. Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  7. Buchtala, O., Klimek, M., Sick, B.: Evolutionary optimization of radial basis function classifiers for data mining classifications. IEEE Transactions on Systems Man and Cybernetics Part B- Cybernetics 35(5), 928–947 (2005)

    Article  Google Scholar 

  8. Corfman, K.P.: Comparability and Comparison Levels Used in Choices Among Consumer Products. Journal of Marketing Research 28(3), 368–374 (1991)

    Article  Google Scholar 

  9. DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988)

    Article  MATH  Google Scholar 

  10. Dietterich, T.G.: Machine-Learning Research – Four current directions. AI Magazine 18(4), 97–136 (1997)

    Google Scholar 

  11. Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. Technical Report HPL-2003-4, HP Laboratories (2003)

    Google Scholar 

  12. Green, D., Swets, J.A.: Signal detection theory and psychophysics. John Wiley & Sons, New York (1966)

    Google Scholar 

  13. Huang, Y., McCullagh, P., Black, N., Harper, R.: Feature Selection and Classification Model Construction on Type 2 Diabetic Patient’s Data. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 153–162. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Johnson, M.D.: Consumer Choice Strategies for Comparing Noncomparable Alternatives. Journal of Consumer Research 11(3), 741–753 (1984)

    Article  Google Scholar 

  15. Knott, A., Hayes, A., Neslin, S.A.: Next-Product-To-Buy Models for Cross-selling Applications. Journal of Interactive Marketing 16(3), 59–75 (2002)

    Article  Google Scholar 

  16. Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97(1-2), 273–324 (1997)

    Article  MATH  Google Scholar 

  17. Leopold, E., Kindermann, J.: Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? Machine Learning 46(1-3), 423–444 (2002)

    MATH  Google Scholar 

  18. Liu, H., Yu, L.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005)

    Article  Google Scholar 

  19. Melgani, F., Bruzzone, L.: Classification of Hyperspectral Remote Sensing Images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing 42(8), 1778–1790 (2004)

    Article  Google Scholar 

  20. Morrison, D.G.: On the interpretation of discriminant analysis. Journal of Marketing Research 6, 156–163 (1969)

    Article  MathSciNet  Google Scholar 

  21. Prinzie, A., Van den Poel, D.: Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM. Decision Support Systems (in press, 2006)

    Google Scholar 

  22. Sindhwani, V., Rakshit, S., Deodhare, D., Erdogmus, D., Principe, J.C., Niyogi, P.: Feature Selection in MLPs and SVMs Based on Maximum Output Information. IEEE Transactions on Neural Networks 15(4), 937–948 (2004)

    Article  Google Scholar 

  23. Xing, B., Jordan, M., Karp, R.: Feature Selection for High-Dimensional Genomic Microarray Data. In: Proc. 15th International Conf. Machine Learning, pp. 601–608 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Marketing, Ghent University, Hoveniersberg 24, 9000, Ghent, Belgium

    Anita Prinzie & Dirk Van den Poel

Authors
  1. Anita Prinzie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dirk Van den Poel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prinzie, A., Van den Poel, D. (2006). Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_25

Download citation

  • DOI: https://doi.org/10.1007/11790853_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36036-0

  • Online ISBN: 978-3-540-36037-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation