Bagging, random subspace method and biding

Abstract

In recent years, many approaches for achieving high performance by combining some classifiers have been proposed. We exploit many random replicates of samples in the bagging, and randomly chosen feature subsets in the random subspace method. In this paper, we introduce a method for selecting both samples and features at the same time and demonstrate the effectiveness of the method. This method includes a parametric bagging and a parametric random subspace method as special cases. In some experiments, this method and the parametric random subspace method showed the best performance. © 2008 Springer Berlin Heidelberg.