Combining multiple classification or regression models using genetic algorithms

Authors:
Andrzej Janusz
Affiliations:
Faculty of Mathematics, Informatics, and Mechanics, The University of Warsaw, Warszawa, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

Blending is a well-established technique, commonly used to increase performance of predictive models. Its effectiveness has been confirmed in practice as most of the latest international data-mining contest winners were using some kind of a committee of classifiers to produce their final entry. This paper is a technical report presenting a method of using a genetic algorithm to optimize an ensemble of multiple classification or regression models. An implementation of this method in R, called Genetic Meta-Blender, was tested during the Australian Data Mining 2009 Analytic Challenge competition and it was awarded with the Grand Champion prize for achieving the best overall result. In the report, the purpose of the challenge is described and details of the winning approach are given. The results of Genetic Meta-Blender are also discussed and compared to several baseline scores.