Integration of Decision Trees Using Distance to Centroid and to Decision Boundary

Abstract

Plethora of ensemble techniques have been implemented and studied in order to achieve better classification results than base classifiers. In this paper an algorithm for integration of decision trees is proposed, which means that homogeneous base classifiers will be used. The novelty of the presented approach is the usage of the simultaneous distance of the object from the decision boundary and the center of mass of objects belonging to one class label in order to determine the score functions of base classifiers. This means that the score function assigned to the class label by each classifier depends on the distance of the classified object from the decision boundary and from the centroid. The algorithm was evaluated using an open-source benchmarking dataset. The results indicate an improvement in the classification quality in comparison to the referential method - majority voting method.