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

Catherine Krier, Michel Mouchart () and Abderrahim Oulhaj
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Catherine Krier: OS Engineer, KPN Group Belgium
Michel Mouchart: Université catholique de Louvain, CORE and ISBA, B-1348 Louvain-la-Neuve, Belgium
Abderrahim Oulhaj: OPTIMA, Nuffield Department of Clinical Medecine, University of Oxford, U.K.

No 2012011, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Keywords: neural network; perceptron; stated preferences; ranking data (search for similar items in EconPapers)
Date: 2012-04-05
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2012011

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