Gene expression programming classifier with concept drift detection based on fisher exact test

Abstract

The paper proposes to use gene expression programming with metagenes as a base classifier integrated with the Fisher exact test drift detector. The approach assumesmaintaining during the classification process two windows, recent and older. If the drift is detected, the recent windowis used to induce a newclassifier with a view to adapt to the drift changes. The idea is validated in the computational experiment where the performance of the GEP-based classifier with Fisher exact test detector is compared with classifiers using Naïve Bayes and Hoeffding tree as the base learners.