GA-Stacking: Evolutionary Stacked Generalization Articles uri icon

publication date

  • March 2010

start page

  • 89

end page

  • 119

issue

  • 1

volume

  • 14

international standard serial number (ISSN)

  • 1088-467X

electronic international standard serial number (EISSN)

  • 1571-4128

abstract

  • Abstract. Stacking is a widely used technique for combining classifiers and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers, their parameters and the
    meta-classifiers was a critical issue. Most of the research on this topic
    hand picks the right combination of classifiers and their parameters.
    Instead of starting from these initial strong assumptions, our approach
    uses genetic algorithms to search for good Stacking configurations. Since
    this can lead to overfitting, one of the goals of this paper is to
    empirically evaluate the overall efficiency of the approach. A second
    goal is to compare our approach with the current best Stacking building
    techniques. The results show that our approach finds Stacking
    configurations that, in the worst case, perform as well as the best
    techniques, with the advantage of not having to manually set up the
    structure of the Stacking system.