C4.5: programs for machine learning
C4.5: programs for machine learning
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
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
International Journal of Approximate Reasoning
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
Initial population construction for convergence improvement of MOEAs
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the Properties of Prototype-Based Fuzzy Classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
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
IEEE Transactions on Fuzzy Systems
Developing a bioaerosol detector using hybrid genetic fuzzy systems
Engineering Applications of Artificial Intelligence
International Journal of Approximate Reasoning
Piecewise parametric polynomial fuzzy sets
International Journal of Approximate Reasoning
Fuzzy qualitative trigonometry
International Journal of Approximate Reasoning
A new method for design and reduction of neuro-fuzzy classification systems
IEEE Transactions on Neural Networks
Two cooperative ant colonies for feature selection using fuzzy models
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
IEEE Transactions on Fuzzy Systems - Special section on computing with words
International Journal of Approximate Reasoning
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Learning fuzzy rules for similarity assessment in case-based reasoning
Expert Systems with Applications: An International Journal
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
A double axis classification of interpretability measures for linguistic fuzzy rule-based systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Modeling with discrete-time recurrent fuzzy systems via mixed-integer optimization
Fuzzy Sets and Systems
Efficient ant colony optimization for image feature selection
Signal Processing
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This paper presents a hybrid method for identification of Pareto-optimal fuzzy classifiers (FCs). In contrast to many existing methods, the initial population for multiobjective evolutionary algorithms (MOEAs) is neither created randomly nor a priori knowledge is required. Instead, it is created by the proposed two-step initialization method. First, a decision tree (DT) created by C4.5 algorithm is transformed into an FC. Therefore, relevant variables are selected and initial partition of input space is performed. Then, the rest of the population is created by randomly replacing some parameters of the initial FC, such that, the initial population is widely spread. That improves the convergence of MOEAs into the correct Pareto front. The initial population is optimized by NSGA-II algorithm and a set of Pareto-optimal FCs representing the trade-off between accuracy and interpretability is obtained. The method does not require any a priori knowledge of the number of fuzzy sets, distribution of fuzzy sets or the number of relevant variables. They are all determined by it. Performance of the obtained FCs is validated by six benchmark data sets from the literature. The obtained results are compared to a recently published paper [H. Ishibuchi, Y. Nojima, Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning, International Journal of Approximate Reasoning 44 (1) (2007) 4-31] and the benefits of our method are clearly shown.