Rotation-based model trees for classification
by S.B. Kotsiantis
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 2, No. 1, 2010

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.

Online publication date: Thu, 03-Dec-2009

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