Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Chance constrained programming with fuzzy parameters
Fuzzy Sets and Systems
Minimax chance constrained programming models for fuzzy decision systems
Information Sciences: an International Journal
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support vector machine for classification based on fuzzy training data
Expert Systems with Applications: An International Journal
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
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This paper aims to improve the performance of the widely used fuzzy support vector machine (FSVM) model. By introducing a fuzzy possibility measure, we first modify the original inequality constraints of FSVM optimization model as chance constraints. We fuzzify the distance between training data and the separating hyperplane, and use a possibility measure to compare two fuzzy numbers in forming the constraints for the FSVM model. By maximizing the confidence level we ensure that the number of misclassifications is minimized and the separation margin is maximized to guarantee the generalization. Then, the fuzzy simulation based genetic algorithm is used to solve the new optimization model. The effectiveness of the proposed model and algorithm is validated on an application to the classification of uncertainty in the hydrothermal sulfide data in the TAG region of ocean survey. The experimental results show that the new fuzzy chance constrained SVM model outperforms the original SVM model.