Randomization in Aggregated Classification Trees

  • Eugeniusz Gatnar
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Tree-based models are popular and widely used because they are simple, flexible and powerful tools for classification. Unfortunately they are not stable classifiers. Significant improvement of model stability and prediction accuracy can be obtained by aggregation of multiple classification trees. The reduction of classification error is a result of decreasing bias or/and variance of the committee of trees (called also an ensemble or a forest). In this paper we discuss and compare different methods for model aggregation. We also address the problem of finding minimal number of trees sufficient for the forest.


Prediction Error Training Sample Random Forest Component Tree Classification Error 
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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Eugeniusz Gatnar
    • 1
  1. 1.Institute of StatisticsKatowice University of EconomicsKatowicePoland

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