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Rotation-based model trees for classification

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  • S.B. Kotsiantis
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    Abstract

    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.

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    Bibliographic Info

    Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.

    Volume (Year): 2 (2010)
    Issue (Month): 1 ()
    Pages: 22-37

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    Handle: RePEc:ids:injdan:v:2:y:2010:i:1:p:22-37

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    Web page: http://www.inderscience.com/browse/index.php?journalID=282

    Related research

    Keywords: machine learning; classifier ensembles; combining models; model trees; classification; decision trees; rotation-based ensemble.;

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