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Neural modelling of ranking data with an application to stated preference data

Author

Listed:
  • KRIER, Catherine

    (OS Engineer, KPN Group Belgium)

  • MOUCHART, Michel

    (Université catholique de Louvain, CORE and ISBA, B-1348 Louvain-la-Neuve, Belgium)

  • OULHAJ, Abderrahim

    (OPTIMA, Nuffield Department of Clinical Medecine, University of Oxford, U.K.)

Abstract

Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model. Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a perceptron-based algorithm for a highly non linear model. Data on stated preferences obtained through a survey by face-to-face interviews, in the field of freight transport, are used to illustrate the method. Numerical difficulties are pinpointed and a Pocket type algorithm is shown to provide an efficient heuristic to minimize the discrete error criterion. A substantial merit of this approach is to provide a workable estimation of contextually interpretable parameters along with a statistical evaluation of the goodness of fit.

Suggested Citation

  • KRIER, Catherine & MOUCHART, Michel & OULHAJ, Abderrahim, 2012. "Neural modelling of ranking data with an application to stated preference data," LIDAM Discussion Papers CORE 2012011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2012011
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    References listed on IDEAS

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