On dynamic selection of subspace for random forest

Random Forest is one of the most popular decision forest building algorithms that use decision trees as the base classifiers. The splitting attributes for decision trees of Random Forest are generally determined from a predefined number of randomly selected attribute subset of the original attribute set. In this paper, we propose a new technique that randomly determines the size of the attribute subset between a dynamically determined range based on the relative size of current data segment to the bootstrap samples at each node splitting event. We present elaborate experimental results involving five widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in the context of Random Forest.