Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Further examination of fuzzy linear regression
Fuzzy Sets and Systems
Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Applying genetic algorithms to search for the best hierarchical clustering of a dataset
Pattern Recognition Letters
Insight of a fuzzy regression model
Fuzzy Sets and Systems
Neuro-genetic approach to multidimensional fuzzy reasoning for pattern classification
Fuzzy Sets and Systems
Partial correlation of fuzzy sets
Fuzzy Sets and Systems
Design of a GA-based fuzzy PID controller for non-minimum phase systems
Fuzzy Sets and Systems
A scalable, incremental learning algorithm for classification problems
Computers and Industrial Engineering
Mining fuzzy association rules for classification problems
Computers and Industrial Engineering
Multi-item fuzzy EOQ models using genetic algorithm
Computers and Industrial Engineering
Design of adaptive fuzzy model for classification problem
Engineering Applications of Artificial Intelligence
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive 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
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This paper proposes a new hierarchical fuzzy model (HFM) to solve the classification problem. The developed classification model comprises of two stages; one is to generate the fuzzy IF-THEN rules for each subsystem and the other is to determine the classification unit. For the classification problem, number of rules and the correct classification rate are the fundamental requirements. In this paper, we also advance two genetic algorithms (GAs) to tune the HFM. One is used to determine the combination of the input features for each subsystem on the HFM and the other is to reduce the number of rules in each fuzzy subsystem. The performance has been tested by simulations on the well known Wine and Iris databases. Simulations demonstrate that the proposed HFM under a few rules can provide sufficiently high classification rate even with higher feature dimensions.