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Improving the classification performance is a crucial step of any machine learning method. In order to achieve a better classification Support Vector Machines need to tune parameters and to select relevant variables. To simultaneously perform both targets an embedded approach can be considered. This method consists of a two-layer algorithm where an evolutionary approach handles the solutions and an...
The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters' values which provide a low prediction error. Moreover,...
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