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

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 ob jects 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.

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Document type	Document de travail (Working Paper)
Access type	Accès libre
Publication date	2007
Language	Anglais
Number of pages	16 pages
Collection	STAT Discussion Paper - 0701
Affiliations	UCL - FSA/ELEC - Département d'électricité UCL - EUEN/STAT - Institut de statistique Oxford University - Department of pharmacology
Keywords	neural network ; perceptron ; stated preferences ; ranking data
Links	http://sites.uclouvain.be/IAP-Stat-Phase-V-VI/ISBApub/ISBAdp.html#2007 http://hdl.handle.net/2078.1/126274[Handle]

Has derivation
	[boreal:110077]	Neural modelling of ranking data with an application to stated preference data
Is derivation of
	[boreal:110077]	Neural modelling of ranking data with an application to stated preference data