Fuzzy measure analysis of public attitude towards the use of nuclear energy
Fuzzy Sets and Systems
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Introduction to the theory of neural computation
Introduction to the theory of neural computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy integral in multicriteria decision making
Fuzzy Sets and Systems - Special issue on fuzzy information processing
Identification of &lgr;-fuzzy measure by genetic algorithms
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases
Fuzzy Sets and Systems
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Neural Networks in Business: Techniques and Applications
Neural Networks in Business: Techniques and Applications
Fuzzy Measure Theory
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning multicriteria fuzzy classification method PROAFTN from data
Computers and Operations Research
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Genetic algorithms for determining fuzzy measures from data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Parameter optimization for growth model of greenhouse crop using genetic algorithms
Applied Soft Computing
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Applying fuzzy measures and nonlinear integrals in data mining
Fuzzy Sets and Systems
Fuzzy Classifier Design
Use of fuzzy-logic-inspired features to improve bacterialrecognition through classifier fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of general fuzzy measures by genetic algorithmsbased on partial information
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Class decomposition for GA-based classifier agents - a Pitt approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Choquet fuzzy integral-based hierarchical networks for decision analysis
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Multiple network fusion using fuzzy logic
IEEE Transactions on Neural Networks
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A multi-layer perceptron with single output node can be served as a classifier for two-class problems. Traditionally, an activation function such as the sigmoid function of a neuron performs the linear multi-regression model, which assumes that there is no interaction among attributes. However, because the interaction should not be ignored, this paper uses a non-linear fuzzy integral to replace the linear form by interpreting the connection weights as the values of the fuzzy measure and the degrees of importance of the respective input signals for the fuzzy integral-based sigmoid function. A fitness function of maximizing the number of correctly classified training patterns and minimizing the errors between the actual and desired outputs of individual training patterns is incorporated into the genetic algorithm to obtain appropriate parameter specifications. The experimental results further demonstrate that the perceptron with the fuzzy integral-based sigmoid function performs well in comparison with the traditional multi-layer perceptron.