Minimizing Number of Nodes in Decision Trees with Hypotheses

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses about values of all attributes. This approach is similar to one studied in exact learning, where membership and equivalence queries are considered. We propose dynamic programming algorithms for the minimization of the number of nodes in such decision trees and discuss results of computer experiments.

Azad, M., Chikalov, I., Hussain, S., & Moshkov, M. (2021). Minimizing Number of Nodes in Decision Trees with Hypotheses. Procedia Computer Science, 192, 232–240. doi:10.1016/j.procs.2021.08.024

Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to anonymous reviewers for useful comments and suggestions.

Elsevier BV

Procedia Computer Science


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