Classification using rough random forest

Abstract

The Rough random forest is a classification model based on rough set theory. The Rough random forest uses the concept of random forest and rough set theory in a single model. It combines a collection of decision trees for classification instead of depending on a single decision tree. It uses the concept of bagging and random subspace method to improve the performance of the classification model. In the rough random forest the reducts of each decision tree are chosen on the basis of boundary region condition. Each decision tree uses a different subset of patterns and features. The class label of patterns is obtained by combining the decisions of all the decision trees by majority voting. Results are reported on a number of benchmark datasets and compared with other techniques. Rough random forest is found to give better performance.