Affiliations: [a] Computational Intelligence Laboratory, Department of Mathematics, University of Patras, Patras, Greece | [b] Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Correspondence:
[*]
Corresponding authors: Stamatios-Aggelos N. Alexandropoulos, Department of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi, Greece, E-mail: stalexan@ee.duth.gr. Christos K. Aridas, Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, GR-26110 Patras, Greece. E-mail: char@upatras.gr.
Abstract: During the last decade, a variety of ensembles methods has been developed. All known and widely used methods of this category produce and combine different learners utilizing the same algorithm as the basic classifiers. In the present study, we use two well-known approaches, namely, Rotation Forest and Random Subspace, in order to increase the effectiveness of a single learning algorithm. We have conducted experiments with other well-known ensemble methods, with 25 sub-classifiers, in order to test the proposed model. The experimental study that we have conducted is based on 35 various datasets. According to the Friedman test, the Rotation Forest of Random Subspace C4.5 (RFRS C4.5) and the PART (RFRS PART) algorithms exhibit the best scores in our resulting ranking. Our results have shown that the proposed method exhibits competitive performance and better accuracy in most of the cases.
Keywords: Ensembles of classifiers, rotation forest, random subspace, machine learning, data mining, classification
