Using Model Trees for Classification

Authors:
Eibe Frank;Yong Wang;Stuart Inglis;Geoffrey Holmes;Ian H. Witten
Affiliations:
Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail: eibe@cs.waikato.ac.nz, yongwang@cs.waikato.ac.nz, singlis@cs.waikato.ac.nz, geoff@cs.waikato.ac.nz, ihw@cs.wai ...;Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail: eibe@cs.waikato.ac.nz, yongwang@cs.waikato.ac.nz, singlis@cs.waikato.ac.nz, geoff@cs.waikato.ac.nz, ihw@cs.wai ...;Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail: eibe@cs.waikato.ac.nz, yongwang@cs.waikato.ac.nz, singlis@cs.waikato.ac.nz, geoff@cs.waikato.ac.nz, ihw@cs.wai ...;Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail: eibe@cs.waikato.ac.nz, yongwang@cs.waikato.ac.nz, singlis@cs.waikato.ac.nz, geoff@cs.waikato.ac.nz, ihw@cs.wai ...;Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail: eibe@cs.waikato.ac.nz, yongwang@cs.waikato.ac.nz, singlis@cs.waikato.ac.nz, geoff@cs.waikato.ac.nz, ihw@cs.wai ...
Venue:
Machine Learning
Year:
1998

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Abstract

Model trees, which are a type of decision tree with linearregression functions at the leaves, form the basis of a recent successfultechnique for predicting continuous numeric values. They can be applied toclassification problems by employing a standard method of transforming aclassification problem into a problem of function approximation.Surprisingly, using this simple transformation the model tree inducerM5′, based on Quinlan‘s M5,generates more accurate classifiers than the state-of-the-art decision treelearner C5.0, particularly when most of the attributesare numeric.