Abstract
The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository.
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Alkhalid, A., Chikalov, I., Hussain, S., Moshkov, M. (2013). Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_2
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DOI: https://doi.org/10.1007/978-3-642-28699-5_2
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