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Accurate Neural Network Ensembles Using Genetic Programming
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2006 (English)In: Proceedings of SAIS: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society, Swedish Artificial Intelligence Society - SAIS, Umeå universitet , 2006Conference paper, Published paper (Other academic)
Abstract [en]

Abstract: In this paper we present and evaluate a novel algorithm for ensemble creation. The main idea of the algorithm is to first independently train a fixed number of neural networks (here ten) and then use genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible to not only consider ensembles of different sizes, but also to use ensembles as intermediate building blocks. The final result is therefore more correctly described as an ensemble of neural network ensembles. The experiments show that the proposed method, when evaluated on 22 publicly available data sets, obtains very high accuracy, clearly outperforming the other methods evaluated. In this study several micro techniques are used, and we believe that they all contribute to the increased performance.

One such micro technique, aimed at reducing overtraining, is the training method, called tombola training, used during genetic evolution. When using tombola training, training data is regularly resampled into new parts, called training groups. Each ensemble is then evaluated on every training group and the actual fitness is determined solely from the result on the hardest part.

Place, publisher, year, edition, pages
Swedish Artificial Intelligence Society - SAIS, Umeå universitet , 2006.
Series
UMINF, ISSN 0348-0542
Identifiers
URN: urn:nbn:se:his:diva-1914OAI: oai:DiVA.org:his-1914DiVA: diva2:32190
Available from: 2007-03-22 Created: 2007-03-22 Last updated: 2013-03-20

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Löfström, TuveKönig, RikardNiklasson, Lars
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