Variable selection for support vector machine based multisensor systems

In this paper, a new variable selection technique inspired in sequential forward selection but specifically designed to work with support vector machines is introduced. The usefulness of the variable selection coupled to support vector machines for solving classification and regression problems is assessed by analysing two different databases. The first database corresponds to different concentrations of vapours and vapour mixtures measured with a metal oxide gas-sensor e-nose and the second database corresponds to different Iberian hams measured with a mass-spectrometry based e-nose. Using a reduced set of important variables (i.e. reducing the dimensionality of input space by the variable selection procedure) results in support vector machines with better performance. For example, the success rate in ham classification (11-class problem) rises from 79.91% (when all the variables available are used) to 90.30% (when a reduced set of input variables is used). Furthermore, a quantitative analysis of ham samples with good accuracy is shown to be possible: when the variable selection process introduced is coupled to support vector machine regression models, the correlation coefficients of actual versus predicted humidity, water activity and salt in ham samples are 0.975, 0.972 and 0.943, respectively. This compares favourably with the correlation coefficients obtained when no variable selection is performed (0.937, 0.924 and 0.894).