Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
On Issues of Instance Selection
Data Mining and Knowledge Discovery
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Parallel distributed genetic fuzzy rule selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
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 Hybrid Intelligent Systems - Hybrid Fuzzy Models
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems - Special section on computing with words
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Multi-objective evolutionary fuzzy systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
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During the last years, multi-objective evolutionary algorithms (MOEAs) have been extensively employed as optimization tools for generating fuzzy rule-based systems (FRBSs) with different trade-offs between accuracy and interpretability from data. Since the size of the search space and the computational cost of the fitness evaluation depend on the number of input variables and instances, respectively, managing high-dimensional and large datasets is a critical issue. In this paper, we focus on MOEAs applied to learn concurrently the rule base and the data base of Mamdani FRBSs and propose to tackle the issue by exploiting the synergy between two different techniques. The first technique is based on a novel method which reduces the search space by learning rules not from scratch, but rather from a heuristically generated rule base. The second technique performs an instance selection by exploiting a co-evolutionary approach where cyclically a genetic algorithm evolves a reduced training set which is used in the evolution of the MOEA. The effectiveness of the synergy has been tested on twelve datasets. Using non-parametric statistical tests we show that, although achieving statistically equivalent solutions, the adoption of this synergy allows saving up to 97.38% of the execution time with respect to a state-of-the-art multi-objective evolutionary approach which learns rules from scratch.