Empirical comparison of resampling methods using genetic neural networks for a regression problem

Authors:
Tadeusz Lasota;Zbigniew Telec;Grzegorz Trawiński;Bogdan Trawiński
Affiliations:
Wrocław University of Environmental and Life Sciences, Dept. of Spatial Management, Wrocław, Poland;Wrocław University of Technology, Institute of Informatics, Wrocław, Poland;Wrocław University of Technology, Faculty of Electronics, Wrocław, Poland;Wrocław University of Technology, Institute of Informatics, Wrocław, Poland
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Year:
2011

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Abstract

In the paper the investigation of m-out-of-n bagging with and without replacement using genetic neural networks is presented. The study was conducted with a newly developed system in Matlab to generate and test hybrid and multiple models of computational intelligence using different resampling methods. All experiments were conducted with real-world data derived from a cadastral system and registry of real estate transactions. The performance of following methods was compared: classic bagging, out-of-bag, Efron's .632 correction, and repeated holdout. The overall result of our investigation was as follows: the bagging ensembles created using genetic neural networks revealed prediction accuracy not worse than the experts' method employed in reality.