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


  • Catherine Krier OS Engineer, KPN Group
  • Michel Mouchart Université Catholique de Louvain
  • Abderrahim Oulhaj University of Oxford



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.


B. APOLLONI, I. ZOPPIS, R. ALEANDRI, A. GALLI, (2001), A neural network based procedure for forecasting bovine semen motility, “Statistica”, LXI (2), 301-313.

M. BEUTHE, CH. BOUFFIOUX, J. DE MAEYER, G. SANTAMARIA, M. VANDRESSE, E. VANDAELE, F. WITLOX, (2005), A multi-criteria methodology for stated preferences among freight transport alternatives, in

AURA REGGIANI and LAURIE SCHINTLER (ed.) Methods and Models in Transport and Communications: Cross Atlantic Perspectives, Berlin: Springer Verlag.

M. BEUTHE M., CH. BOUFFIOUX, C. KRIER, M. MOUCHART, (2008), A comparison of conjoint, multicriteria, conditional logit and neural network analyses for rank-ordered preference data, chap. 9 in M.

BEN-AKIVA, H. MEERSMAN and E. VAN DE VOORDE (ed.) Recent Developments in Freight Transport Modelling: Lessons from the Freight Sector, Emerald Group Publishing Limited, 157-178.

M. BEUTHE, G. SCANNELLA, (2001), Comparative analysis of UTA multi-criteria methods, ‘European Journal of operational research’, 130 (2): 246-262.

E. BIGANZOLI, P. BORACCHI, I. POLI, (2000), Reti neurali artificiali per lo studio di fenomeni complessi: limiti e vantaggi delle applicazioni in biostatistica, ‘Statistica’, LX (4), 723-734

C.M. BISHOP, (1995), Neural Networks for Pattern Recognition, Oxford: Oxford University Press.

S.I. GALLANT, (1996), Perceptron based learning algorithms, ‘IEEE Transactions on Neural Networks’, 1:179-191.

S. HAYKIN, (1999), Neural Networks: a comprehensive foundation, 2nd ed., Prentice-Hall, Inc.

A. OULHAJ, M. MOUCHART, (2003), Partial sufficiency with connection to the identification problem, ‘Metron’, LXI (2), 267-683.

M. PILLATI, (2001), Le reti neurali in problemi di classificazione: una soluzione basata sui metodi di segmentazione binaria, ‘Statistica’, LXI (3), 407-421 H.R. VARIAN, (1992), Microeconomic Analysis, 3rd ed., Norton & Company, New-York.




How to Cite

Krier, C., Mouchart, M., & Oulhaj, A. (2012). Neural modelling of ranking data with an application to stated preference data. Statistica, 72(3), 255–269.