Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Fuzzy Sets and Systems - Special issue on clustering and learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy Data Mining: Effect of Fuzzy Discretization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A modified pittsburg approach to design a genetic fuzzy rule-based classifier from data
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
An interval type-2 fuzzy logic system for the modeling and prediction of financial applications
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
A new fuzzy rule-based classification system for word sense disambiguation
Intelligent Data Analysis
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This paper compares heuristic criteria used for extracting a pre-specified number of fuzzy classification rules from numerical data. We examine the performance of each heuristic criterion through computational experiments on well-known test problems. Experimental results show that better results are obtained from composite criteria of confidence and support measures than their individual use. It is also shown that genetic algorithm-based rule selection can improve the classification ability of extracted fuzzy rules by searching for good rule combinations. This observation suggests the importance of taking into account the combinatorial effect of fuzzy rules (i.e., the interaction among them).