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
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
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
Feature Selection for Ensembles: A Hierarchical Multi-Objective Genetic Algorithm Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
Fuzzy Optimization and Decision Making
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
International Journal of Approximate Reasoning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
Multi-objective genetic algorithms to create ensemble of classifiers
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Agent-based evolutionary approach for interpretable rule-based knowledge extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hybridization of fuzzy GBML approaches for pattern classification problems
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
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
International Journal of Approximate Reasoning
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases
International Journal of Hybrid Intelligent Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we examine the application of evolutionary multiobjective optimization (EMO) algorithms to the design of fuzzy rule-based ensemble classifiers. An EMO algorithm is used to search for a large number of non-dominated fuzzy rule-based classifiers along the accuracy-complexity tradeoff surface. The accuracy of each fuzzy rule-based classifier is measured by the number of correctly classified training patterns while its complexity is measured by the number of fuzzy rules and the total number of antecedent conditions. An ensemble classifier is designed by combining non-dominated fuzzy rule-based classifiers. We examine the performance of ensemble classifiers through computational experiments on six benchmark data sets in the UCI machine learning repository. We also examine the diversity of individual fuzzy rule-based classifiers in each ensemble classifier.