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

Catherine Krier (), Michel Mouchart () and Abderrahim Oulhaj ()
Additional contact information
Catherine Krier: OS Engineer, KPN Group - Belgium
Michel Mouchart: CORE and ISBA, Université catholique de Louvain - Belgium
Abderrahim Oulhaj: DTU, Nuffield Department of Clinical Medecine - University of Oxford - UK

Statistica, 2012, vol. 72, issue 3, 255-269

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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:72:y:2012:i:3:p:255-269

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