A survey of fuzzy decision tree classifier |
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Authors: | Yi-lai Chen Tao Wang Ben-sheng Wang Zhou-jun Li |
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Institution: | 1. Nanjing Army Command College, Nanjing, 210045, P.R.China 2. School of Computer Science & Engineering, Beihang University, Beijing, 100083, P.R.China
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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. |
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