Minimizing Number of Nodes in Decision Trees with Hypotheses

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish (US)
Pages (from-to)232-240
Number of pages9
JournalProcedia Computer Science
Volume192
DOIs
StatePublished - 2021

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