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Bien qu’ancienne, l’intégrale de Choquet fait l’objet d’une littérature récente et abondante en recherche opérationnelle. Ces travaux ouvrent des perspectives d’applications intéressantes dans le domaine marketing, notamment dans la mise en oeuvre des systèmes de recommandations s’appuyant sur des critères multiples. L’intégrale de Choquet est intéressante dans l’élaboration d’un modèle d’attitude car elle permet de prendre en compte les relations de dépendance entre critères. Ce papier propose une utilisation pratique de l’intégrale de Choquet dans un configurateur de choix ou configurateur de recommandations basé sur un filtrage actif multicritères.
The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in these studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.
