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.
Cite
CITATION STYLE
APA
Knoll, U., Nakhaeizadeh, G., & Tausend, B. (1994). Cost-Sensitive pruning of decision trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 784 LNCS, pp. 383–386). Springer Verlag. https://doi.org/10.1007/3-540-57868-4_79
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