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Hierarchical Clustering of Multiple Decision Trees

  • Branko Kavšek
  • Nada Lavrač
  • Anuška Ferligoj
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Decision tree learning is relatively non-robust: a small change in the training set may significantly change the structure of the induced decision tree. This paper presents a decision tree construction method in which the domain model is constructed by consensus clustering of N decision trees induced in N-fold cross-validation. Experimental results show that consensus decision trees are simpler than C4.5 decision trees, indicating that they may be a more stable approximation of the intended domain model than decision trees, constructed from the entire set of training instances.

Keywords

Decision Tree Consensus Cluster Concept Hierarchy Decision Tree Learning Decision Tree Induction 
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 2002

Authors and Affiliations

  • Branko Kavšek
    • 1
  • Nada Lavrač
    • 1
  • Anuška Ferligoj
    • 2
  1. 1.Institute Jožef StefanLjubljanaSlovenia
  2. 2.University of LjubljanaLjubljanaSlovenia

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