C4.5: programs for machine learning
C4.5: programs for machine learning
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Machine Learning
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
International Journal of Approximate Reasoning
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
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Fuzzy Inference Systems (FIS) are much considerable due to their interpretability and uncertainty factors. Hence, Fuzzy Rule-Based Classifier Systems (FRECS) are widely investigated in aspects of construction and parameter learning. Also, decision trees are recursive structures which are not only simple and accurate, but also are fast in classification due to partitioning the feature space in a multi-stage process. Combination of fuzzy reasoning and decision trees gathers capabilities of both systems in an integrated one. In this paper, a novel fuzzy decision tree (FDT) is proposed for extracting fuzzy rules which are both accurate and cooperative due to dependency structure ofdecision tree. Furthermore, a weighting method is used to emphasize the corporation of the rules. Finally, the proposed method is compared with a well-known rule construction method named SRC on 8 UCI datasets. Experiments show a significant improvement on classification performance of the proposed method in comparison with SRC.