Dimensionality of random subspaces

Abstract

Significant improvement of classification accuracy can be obtained by aggregation of multiple models. Proposed methods in this field are mostly based on sampling cases from the training set, or changing weights for cases. Reduction of classification error can also be achieved by random selection of variables to the training subsamples or directly to the model. In this paper we propose a method of feature selection for ensembles that significantly reduces the dimensionality of the subspaces. © Springer-Verlag Berlin, Heidelberg 2005.