Construction of Optimal Decision Trees and Deriving Decision Rules from Them

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko

Research output: Chapter in Book/Report/Conference proceedingChapter


In this chapter, we propose dynamic programming algorithms for the construction of decision trees with minimum depth and decision trees with minimum number of working (nonterminal) nodes. We make computer experiments on various data sets from the UCI Machine Learning Repository and randomly generated Boolean functions to compare the length and coverage of decision rules derived from different kinds of optimal decision trees. The obtained results show that the decision rules derived from optimal decision trees with hypotheses in many cases are better than the rules derived from optimal conventional decision trees.
Original languageEnglish (US)
Title of host publicationDecision Trees with Hypotheses
PublisherSpringer International Publishing
Number of pages13
ISBN (Print)9783031085840
StatePublished - Nov 19 2022


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