Top-down induction of decision trees classifiers - A survey

Lior Rokach, Oded Maimon

Research output: Contribution to journalArticlepeer-review

462 Scopus citations

Abstract

Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.

Original languageEnglish
Pages (from-to)476-487
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume35
Issue number4
DOIs
StatePublished - 1 Nov 2005
Externally publishedYes

Keywords

  • Classification
  • Decision trees
  • Pruning methods
  • Splitting criteria

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