Abstract
This paper presents some empirical results on simplification methods of decision trees induced from data. We observe that those methods exploiting an independent pruning set do not perform uniformly better than the others. Furthermore, a clear definition of bias towards overpruning and underpruning is exploited in order to interpret empirical data concerning the size of the simplified trees.
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© 1995 Springer-Verlag Berlin Heidelberg
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Esposito, F., Malerba, D., Semeraro, G. (1995). Simplifying decision trees by pruning and grafting: New results (Extended abstract). In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_69
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DOI: https://doi.org/10.1007/3-540-59286-5_69
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