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
Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
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
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
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Fuzzy Classifier Design
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
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
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
Fuzzy Optimization and Decision Making
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
International Journal of Approximate Reasoning
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
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
Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fuzzy rule classifier: Capability for generalization in wood color recognition
Engineering Applications of Artificial Intelligence
Optimized fuzzy decision tree using genetic algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Effects of removing overlapping solutions on the performance of the NSGA-II algorithm
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. This paper shows how this advantage can be utilized in genetic rule selection for the design of fuzzy rule-based classification systems. Our genetic rule selection is a two-stage approach. In the first stage, a pre-specified number of candidate rules are extracted from numerical data using a data mining technique. In the second stage, an EMO algorithm is used for finding non-dominated rule sets with respect to three objectives: to maximize the number of correctly classified training patterns, to minimize the number of rules, and to minimize the total rule length. Since the first objective is measured on training patterns, the evolution of rule sets tends to overfit to training patterns. The question is whether the other two objectives work as a safeguard against the overfitting. In this paper, we examine the effect of the three-objective formulation on the generalization ability (i.e., classification rates on test patterns) of obtained rule sets through computer simulations where many non-dominated rule sets are generated using an EMO algorithm for a number of high-dimensional pattern classification problems.