Greedy algorithm for construction of decision trees for tables with many-valued decisions

Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Beata Zielosko

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In the paper, we study a greedy algorithm for construction of approximate 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.We use an uncertainty measure which is the number of boundary subtables. We present also experimental results for data sets from UCI Machine Learning Repository for proposed approach and approach based on generalized decision.

Original languageEnglish (US)
Pages (from-to)13-24
Number of pages12
JournalCEUR Workshop Proceedings
Volume928
StatePublished - 2012

Keywords

  • Decision table with many-valued decisions
  • Decision tree
  • Greedy algorithm

ASJC Scopus subject areas

  • Computer Science(all)

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