Comparison of Greedy Algorithms for Decision Tree Optimization

Abdulaziz Alkhalid, Igor Chikalov, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values of average depth, depth, number of nodes, number of terminal nodes, and number of nonterminal nodes of decision trees. We compare average depth, depth, number of nodes, number of terminal nodes and number of nonterminal nodes of constructed trees with minimum values of the considered parameters obtained based on a dynamic programming approach. We report experiments performed on data sets from UCI ML Repository and randomly generated binary decision tables. As a result, for depth, average depth, and number of nodes we propose a number of good heuristics. © Springer-Verlag Berlin Heidelberg 2013.
Original languageEnglish (US)
Pages (from-to)21-40
Number of pages20
JournalIntelligent Systems Reference Library
Volume43
DOIs
StatePublished - 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Library and Information Sciences
  • Computer Science(all)
  • Information Systems and Management

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