Minimizing Depth of Decision Trees with Hypotheses

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. Such decision trees are similar to ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions.
Original languageEnglish (US)
Title of host publicationRough Sets
PublisherSpringer International Publishing
Pages123-133
Number of pages11
DOIs
StatePublished - Sep 16 2021

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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