Amal Bouraoui

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The Support Vector Machines (SVM) constitute a very powerful technique for pattern classification problems. However, its efficiency in practice depends highly on the selection of the kernel function type and relevant parameter values. Selecting relevant features is another factor that can also impact the performance of SVM. The identification of the best(More)
The present article introduces a genetic algorithm based method to select interesting features in a clustering framework. Indeed, we used the evolutionary paradigm to explore many subsets of attributes and evaluate them according to inertia criteria when the K-Means clustering method is used in different way to data clusters. The proposed method is applied(More)
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