Exponentially Weighted Random Forest

Abstract

Random forest (RF) is a supervised, non-parametric, ensemble-based machine learning method used for classification and regression task. It is easy in terms of implementation and scalable, hence attracting many researchers. Being an ensemble-based method, it considers equal weights/votes to all atomic units i.e. decision trees. However, this may not be true always for varying test cases. Hence, the correlation between decision tree and data samples are explored in the recent past to take care of such issues. In this paper, a dynamic weighing scheme is proposed between test samples and decision tree in RF. The correlation is defined in terms of similarity between the test case and the decision tree using exponential distribution. Hence, the proposed method named as Exponentially Weighted Random Forest (EWRF). The performance of the proposed method is rigorously tested over benchmark datasets from the UCI repository for both classification and regression tasks.