A survey of fuzzy decision tree classifier

Yi-lai Chen, Tao Wang (), Ben-sheng Wang and Zhou-jun Li
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Yi-lai Chen: Nanjing Army Command College
Tao Wang: Nanjing Army Command College
Ben-sheng Wang: Nanjing Army Command College
Zhou-jun Li: Beihang University

Fuzzy Information and Engineering, 2009, vol. 1, issue 2, 149-159

Abstract: Abstract Decision-tree algorithm provides one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, some researchers have proposed to utilize fuzzy representation in decision trees to deal with similar situations. This paper presents a survey of current methods for Fuzzy Decision Tree (FDT) designment and the various existing issues. After considering potential advantages of FDT classifiers over traditional decision tree classifiers, we discuss the subjects of FDT including attribute selection criteria, inference for decision assignment and stopping criteria. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.

Keywords: Fuzzy decision tree; Classifier; Attribute selection; Decision assignment; Stopping criteria (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s12543-009-0012-2

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