Abstract
A new ensemble of support vector machines (SVM) based on random subspace (RS) and feature selection is developed and applied to the problem of differential diagnosis of erythemato-squamous diseases. Each classifier has a “favourite” class. To find the feature subset for the classifier D i with “favourite” class w i , we calculate the best features to discriminate this class ( w i ) from all the other classes. Our results improved the average predictive accuracy obtained by a “stand-alone” SVM or by a RS ensemble of SVM.
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