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.
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Lasota, T., Telec, Z., Trawiński, G., Trawiński, B. (2011). Empirical Comparison of Resampling Methods Using Genetic Neural Networks for a Regression Problem. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_26
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DOI: https://doi.org/10.1007/978-3-642-21222-2_26
Publisher Name: Springer, Berlin, Heidelberg
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