Feature Selection for Bagging of Support Vector Machines

Abstract

Feature selection for the individuals of bagging is studied in this paper. Ensemble learning like bagging can effectively improve the performance of single learning machines, and so can feature selection, but few has studied whether feature selection could improve bagging of single learning machines. Therefore, two typical feature selection approaches namely the embedded feature selection model with the prediction risk criteria and the filter model with the mutual information criteria are used for the bagging of support vector machines respectively. Experiments performed on the UCI data sets show the effectiveness of feature selection for the bagging of support vector machines.