Two ensemble classifiers constructed from GEP-induced expression trees

Authors:
Joanna Jedrzejowicz;Piotr Jędrzejowicz
Affiliations:
Institute of Informatics, Gdańsk University, Gdańsk, Poland;Department of Information Systems, Gdynia Maritime University, Gdynia, Poland
Venue:
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Year:
2010

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Abstract

In this paper we propose two ensemble classifiers using expression trees as weak classifiers. The first ensemble uses the AdaBoost approach and the second makes use of Dempster'â ĂŹs rule of combination and applies triplet mass functions to combine classifiers. The performance of both ensemble classifiers is evaluated experimentally. The experiment involved 9 well known datasets from the UCI Irvine Machine Learning Repository. Experiment results show that using GEP-induced expression trees allows to construct high quality ensemble classifiers.