Comparing Simplification Methods for Model Trees with Regression and Splitting Nodes

  • Michelangelo Ceci
  • Annalisa Appice
  • Donato Malerba
Conference paper

DOI: 10.1007/978-3-540-39592-8_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)
Cite this paper as:
Ceci M., Appice A., Malerba D. (2003) Comparing Simplification Methods for Model Trees with Regression and Splitting Nodes. In: Zhong N., Raś Z.W., Tsumoto S., Suzuki E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science, vol 2871. Springer, Berlin, Heidelberg

Abstract

In this paper we tackle the problem of simplifying tree-based regression models, called model trees, which are characterized by two types of internal nodes, namely regression nodes and splitting nodes. We propose two methods which are based on two distinct simplification operators, namely pruning and grafting. Theoretical properties of the methods are reported and the effect of the simplification on several data sets is empirically investigated. Results are in favor of simplified trees in most cases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Michelangelo Ceci
    • 1
  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli StudiBariItaly

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