Decision tree ensemble construction incorporating feature values modification and random subspace method

The goal of ensemble construction with several classifiers is to achieve better generalization ability over individual classifiers. An ensemble method produces diverse classifiers and combines their decisions for ensemble's decision. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a decision tree ensemble method incorporating some generated patterns with random subspace method (RSM). The proposed hybrid ensemble method were evaluated on several benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods.