Rotation-based model trees for classification
AbstractStructurally, 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 InfoArticle provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.
Volume (Year): 2 (2010)
Issue (Month): 1 ()
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Web page: http://www.inderscience.com/browse/index.php?journalID=282
machine learning; classifier ensembles; combining models; model trees; classification; decision trees; rotation-based ensemble.;
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