Dynamic Programming Algorithms for Minimization of Decision Tree Complexity

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

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish (US)
Title of host publicationDecision Trees with Hypotheses
PublisherSpringer International Publishing
Number of pages22
ISBN (Print)9783031085840
StatePublished - Nov 19 2022


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