Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions

Mohammad Azad, Mikhail Moshkov

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

6 Scopus citations

Abstract

The paper is devoted to the analysis of greedy algorithms for the minimization of average depth of decision trees for decision tables such that each row is labeled with a set of decisions. The goal is to find one decision from the set of decisions. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of average depth of decision trees.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Pages368-377
Number of pages10
DOIs
StatePublished - Sep 13 2014

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