A Survey of Fuzzy Decision Tree Classifier Methodology

摘要

Decision-tree algorithms provide 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 FDT(Fuzzy Decision Tree)designs and the various existing issues. After considering potential advantages of FDTs over traditional decision tree classifiers, the subjects of FDT attribute selection criteria, inference for decision assignment, and decision and stopping criteria are discussed. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.