Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses of values of all attributes. Such decision trees are similar to those studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithm based on entropy for the construction of the above decision trees and discuss the results of computer experiments on various data sets and randomly generated Boolean functions.
Original languageEnglish (US)
Pages (from-to)808
JournalEntropy
Volume23
Issue number7
DOIs
StatePublished - Jun 25 2021

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Statistical and Nonlinear Physics

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