Genetic Programming of Heterogeneous Ensembles for Classification

  • Hugo Jair Escalante
  • Niusvel Acosta-Mendoza
  • Alicia Morales-Reyes
  • Andrés Gago-Alonso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneous-classifiers outputs. It evolves a population of fusion functions in order to maximize the classification accuracy. Highly non-linear functions are obtained with the proposed method, subsuming the existing weighted-sum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneous-classifiers and under bagging/boosting based formulations.

Keywords

Heterogeneous ensembles Genetic programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hugo Jair Escalante
    • 1
  • Niusvel Acosta-Mendoza
    • 1
    • 2
  • Alicia Morales-Reyes
    • 1
  • Andrés Gago-Alonso
    • 2
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico
  2. 2.Advanced Technologies Application Center (CENATAV)HavanaCuba

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