A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Beata Zielosko

Research output: Contribution to journalArticlepeer-review

Abstract

In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalFundamenta Informaticae
Volume128
Issue number1-2
DOIs
StatePublished - Nov 25 2013

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Algebra and Number Theory
  • Theoretical Computer Science
  • Information Systems

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