Experimental Evaluation of Resampling Combined with Clustering and Random Oracle Using Genetic Fuzzy Systems

  • Tadeusz LasotaEmail author
  • Zbigniew Telec
  • Bogdan Trawiński
  • Grzegorz Trawiński
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)


The ensemble methods combining resampling techniques: cross-validation, repeated holdout, and bootstrap sampling with clustering and random oracle using a genetic fuzzy rule-based system as a base learning algorithm were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The computationally intensive experiments were conducted aimed to compare the accuracy of ensembles generated by the proposed methods with different number of clusters or random oracle subsets. The statistical analysis of results was made employing nonparametric Friedman and Wilcoxon statistical tests.


Mean Square Error Fuzzy System Random Oracle Training Instance Resampling Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tadeusz Lasota
    • 1
    Email author
  • Zbigniew Telec
    • 2
  • Bogdan Trawiński
    • 2
  • Grzegorz Trawiński
    • 3
  1. 1.Department of Spatial ManagementWrocław University of Environmental and Life SciencesWrocławPoland
  2. 2.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  3. 3.Faculty of ElectronicsWrocław University of TechnologyWrocławPoland

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