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
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
International Journal of Approximate Reasoning
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
IEEE Transactions on Fuzzy Systems
Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Approximate Reasoning
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
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
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
IEEE Transactions on Neural Networks
Bi-objective feature selection for discriminant analysis in two-class classification
Knowledge-Based Systems
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The main advantage of multi-objective genetic fuzzy systems (MoGFS) is that a number of non-dominated fuzzy rule-based systems are obtained along the tradeoff surface among conflicting objectives. Accuracy maximization, complexity minimization and interpretability maximization have often been used for multi-objective design of fuzzy rule-based classifiers. A number of non-dominated fuzzy rule-based classifiers are obtained by a single run of MoGFS. A human decision maker is supposed to choose a single final classifier from a number of obtained classifiers according to his/her preference. One problem, which has not been discussed in many studies on MoGFS, is how to choose a single final classifier. In this paper, we discuss classifier selection with no intervention of the decision maker. Whereas complexity and interpretability are very important factors in classifier selection, we concentrate on the maximization of generalization ability as the first step towards a more general handling of classifier selection. We propose the use of repeated double cross-validation (rdCV) to choose a single final classifier and to evaluate the generalization ability of the selected classifier. We also discuss how our approach can be applied to parameter specification, formulation selection and algorithm choice.