Problem doboru zmiennych do modelu dyskryminacyjnego budowanego metodą wektorów nośnych - Trzęsiok, Michał (Variable selection in Support Vector Machines, 2009)
Support Vector Machines (SVM) belong to the group of Data Mining methods and are considered as a black box method. Some authors suggest that variable selection is usually not necessary for SVMs, i.e. building the model on a set of variables including some (but not too many) redundant variables does not change the generalization ability. Once the model is built, it is still valuable to recognize the relative importance of predictor variables. The paper presents the simple modification of the backward elimination technique for feature selection and empirically shows that deleting the redundant variables can improve the classification accuracy and reduce the complexity of SVM models. (original abstract)
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu = Research Papers of Wrocław University of Economics; 2009; Nr 47, s. 214-222