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Two Ensemble Classifiers Constructed from GEP-Induced Expression Trees

  • Joanna Jȩdrzejowicz
  • Piotr Jȩdrzejowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6071)

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’âĂŹs 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.

Keywords

gene expression programming AdaBoost evidentional reasoning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joanna Jȩdrzejowicz
    • 1
  • Piotr Jȩdrzejowicz
    • 2
  1. 1.Institute of InformaticsGdańsk UniversityGdańskPoland
  2. 2.Department of Information SystemsGdynia Maritime UniversityGdyniaPoland

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