Feature Selection using an SVM learning machine

In this paper we suggest an approach to select features for the support vector machines (SVM). Feature selection is efficient in searching the most descriptive features which would contribute in increasing the effectiveness of the classifier algorithm. The process described here consists in backward elimination strategy based on the criterion of the rate of misclassification. We used the tabu algorithm to guide the search of the optimal set of features; each set of features is assessed according to its goodness of fit. This procedure is exploited in the regulation of urban transport network systems. It was first applied in a binary case and then it was extended to the multiclass case thanks to the MSVM technique: binary tree.