Abstract
In this chapter, we present dynamic programming algorithms for minimization of the depth and number of nodes of decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. Decision trees with hypotheses, generally, have less complexity than conventional decision trees, i.e., they are more understandable and more suitable as a means for knowledge representation.
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References
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Azad, M., Chikalov, I., Hussain, S., Moshkov, M., Zielosko, B. (2022). Dynamic Programming Algorithms for Minimization of Decision Tree Complexity. In: Decision Trees with Hypotheses. Synthesis Lectures on Intelligent Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-08585-7_3
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DOI: https://doi.org/10.1007/978-3-031-08585-7_3
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