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This work is motivated by a real work problem: objectivization. It consistsin explaining the subjective drivability using physical criteria coming fromsignals measured during experiments. We suggest an approach for thediscriminant variables selection trying to take advantage of the functionalnature of the data. The porblem is ill-posed, since the number of explanatoryvariables is hugely greater than the sample size. The strategy proceeds inthree steps: a signal preprocessing including wavelet denoising andsynchronization, dimensionality reduction by compression using a common waveletbasis, and finally the selection of useful variables using a stepwise strategyinvolving successive applications of the CART method.

Classification supervis\'ee en grande dimension. Application \`a l'agr\'ement de conduite automobile