Fuzzy measure analysis of public attitude towards the use of nuclear energy
Fuzzy Sets and Systems
The mathematical foundations of learning machines
The mathematical foundations of learning machines
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
Some quantities represented by the Choquet integral
Fuzzy Sets and Systems
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
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases
Fuzzy Sets and Systems
Choquet integral and fuzzy measures on locally compact space
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
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
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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
Choquet fuzzy integral-based hierarchical networks for decision analysis
IEEE Transactions on Fuzzy Systems
The balancing Choquet integral
Fuzzy Sets and Systems
Fuzzy Sets and Systems
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The sigmoid function is usually used as the activation function for a well-known classification method, namely the single-layer perceptron. In the function, a weighted sum, in which the additivity among individual variables is assumed, is performed. However, it is known that an assumption of additivity may not be reasonable, since the input variables are not always independent of each other. This paper thus employs a Choquet fuzzy integral-based neuron as an output neuron of the single-layer perceptron. Moreover, the connection weights can be interpreted as fuzzy measure values or degrees of importance of the respective attributes. The connection weights are determined by the genetic algorithms in which the maximization of the training classification performance and the minimization of the errors between the actual and desired outputs of individual training patterns are taken into account. The experimental results further demonstrate that the classification results of the single-layer perceptron with a Choquet fuzzy integral-based neuron are comparable to those of the traditional single-layer perceptron and the other fuzzy classification methods.