Effects of dynamic subspacing in random forest

Abstract

Due to its simplicity and good performance, Random Forest attains much interest from the research community. The splitting attribute at each node of a decision tree for Random Forest is determined from a predefined number of randomly selected attributes (a subset of the entire attribute set). The size of an attribute subset (subspace) is one of the most important factors that stems multitude of influences over Random Forest. In this paper, we propose a new technique that dynamically determines the size of subspaces based on the relative size of the current data segment to the entire data set. In order to assess the effects of the proposed technique, we conduct experiments involving five widely used data set from the UCI Machine Learning Repository. The experimental results indicate the capability of the proposed technique on improving the ensemble accuracy of Random Forest.