GA-Stacking: Evolutionary Stacked Generalization Articles
Overview
published in
- Intelligent Data Analysis Journal
publication date
- March 2010
start page
- 89
end page
- 119
issue
- 1
volume
- 14
Digital Object Identifier (DOI)
full text
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.