Rotation Forest with GEP-Induced Expression Trees

摘要

In this paper we propose integrating two techniques used in the field of the supervised machine learning. They include rotation forest and gene expression programming. The idea is to build a rotation forest based classifier ensembles using independently induced expression trees. To induce expression trees we apply gene expression programming. The paper includes an overview of the proposed approach. To evaluate the approach computational experiment has been carried out. Its results confirm high quality of the proposed ensemble classifiers integrating rotation forest with gene expression programming.