Using Model Trees for Classification |
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Authors: | Frank Eibe Wang Yong Inglis Stuart Holmes Geoffrey Witten Ian H |
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Affiliation: | (1) Department of Computer Science, University of Waikato, Hamilton, New Zealand. E-mail |
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Abstract: | Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric. |
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Keywords: | Model trees classification algorithms M5 C5 0 decision trees |
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