Title :
Top-down induction of decision trees classifiers - a survey
Author :
Rokach, Lior ; Maimon, Oded
Author_Institution :
Dept. of Ind. Eng., Tel-Aviv Univ., Ramat Aviv, Israel
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.
Keywords :
decision trees; learning by example; pattern classification; regression analysis; tree searching; data mining; decision trees classifiers; machine learning; pattern recognition; pruning method; top-down induction; Classification tree analysis; Data mining; Decision trees; Industrial training; Loans and mortgages; Machine learning; Machine learning algorithms; Pattern recognition; Predictive models; Statistics; Classification; decision trees; pruning methods; splitting criteria;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2004.843247