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Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal

  • Magdalena Graczyk
  • Tadeusz Lasota
  • Bogdan Trawiński
  • Krzysztof Trawiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5991)

Abstract

The experiments, aimed to compare three methods to create ensemble models implemented in a popular data mining system called WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, linear regression, and support vector machine were used to construct ensemble models. All algorithms were employed to real-world datasets derived from the cadastral system and the registry of real estate transactions. Nonparametric Wilcoxon signed-rank tests to evaluate the differences between ensembles and original models were conducted. The results obtained show there is no single algorithm which produces the best ensembles and it is worth to seek an optimal hybrid multi-model solution.

Keywords

ensemble models bagging stacking boosting property valuation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Magdalena Graczyk
    • 1
  • Tadeusz Lasota
    • 2
  • Bogdan Trawiński
    • 1
  • Krzysztof Trawiński
    • 3
  1. 1.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  2. 2.Dept. of Spatial ManagementWrocław University of Environmental and Life SciencesWroclawPoland
  3. 3.European Centre for Soft ComputingEdificio Científico-Tecnológico, 3a PlantaMieres, AsturiasSpain

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