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Rotation-based model trees for classification


  • S.B. Kotsiantis


Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees were coupled with a rotation-based ensemble for solving classification problems. In order to apply this regression technique to classification problems, we considered the conditional class probability function and sought a model-tree approximation to it. During classification, the class whose model tree generated the greatest approximated probability value was chosen as the predicted class. We performed a comparison with other well-known ensembles of decision trees on standard benchmark data sets, and the performance of the proposed technique was greater in most cases.

Suggested Citation

  • S.B. Kotsiantis, 2010. "Rotation-based model trees for classification," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(1), pages 22-37.
  • Handle: RePEc:ids:injdan:v:2:y:2010:i:1:p:22-37

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