Abstract
The pruning of decision trees often relies on the classification accuracy of the decision tree. In this paper, we show how the misclassification costs, a related criterion applied if errors vary in their costs, can be integrated in several well-known pruning techniques.
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© 1994 Springer-Verlag Berlin Heidelberg
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Knoll, U., Nakhaeizadeh, G., Tausend, B. (1994). Cost-sensitive pruning of decision trees. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_79
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DOI: https://doi.org/10.1007/3-540-57868-4_79
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