IDEAS home Printed from https://ideas.repec.org/p/rug/rugwps/07-435.html
   My bibliography  Save this paper

Random Forrests for Multiclass classification: Random Multinomial Logit

Author

Listed:
  • A. PRINZIE

    ()

  • D. VAN DEN POEL

    ()

Abstract

Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into feature selection. Surprisingly, in sharp contrast with binary logit, current software packages lack any feature selection algorithm for MultiNomial Logit. Conversely, Random Forests, another algorithm learning multi class problems, is just like MNL robust but unlike MNL it easily handles high-dimensional feature spaces. This paper investigates the potential of applying the Random Forests principles to the MNL framework. We propose the Random MultiNomial Logit (RMNL), i.e. a random forest of MNLs, and compare its predictive performance to that of a) MNL with expert feature selection, b) Random Forests of classification trees. We illustrate the Random MultiNomial Logit on a cross-sell CRM problem within the home-appliances industry. The results indicate a substantial increase in model accuracy of the RMNL model to that of the MNL model with expert feature selection.

Suggested Citation

  • A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:07/435
    as

    Download full text from publisher

    File URL: http://wps-feb.ugent.be/Papers/wp_07_435.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    2. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    3. Johnson, Michael D, 1984. " Consumer Choice Strategies for Comparing Noncomparable Alternatives," Journal of Consumer Research, Oxford University Press, vol. 11(3), pages 741-753, December.
    4. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    2. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
    3. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    4. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    5. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    Keywords

    multiclass classifier design and evaluation; feature evaluation and selection; data mining methods and algorithms; customer relationship management (CRM);

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rug:rugwps:07/435. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nathalie Verhaeghe). General contact details of provider: http://edirc.repec.org/data/ferugbe.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.