Fuzzy measure analysis of public attitude towards the use of nuclear energy
Fuzzy Sets and Systems
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
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases
Fuzzy Sets and Systems
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
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
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Modeling derived from Bayesian filtering: analysis of estimation process
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
A generalization of universal integrals by means of level dependent capacities
Knowledge-Based Systems
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
A fuzzy integral fusion approach in analyzing competitiveness patterns from WCY2010
Knowledge-Based Systems
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This paper presents a novel multi-layer perceptron using a non-additive decision making method and applies this model to the financial distress analysis, which is an important classification problem for a business, and the multi-layer perceptron has played a significant role in financial distress analysis. Traditionally, an activation function of an output neuron performs an additive method, namely the weighted sum method. Since the assumption of additivity among individual variables may not be reasonable, this paper uses a non-additive method, Choquet fuzzy integral, the fuzzy integral, to replace the weighted sum. In order to determine appropriate parameter specifications in the proposed model, a genetic algorithm is designed by considering the maximization of the number of correctly classified training patterns and the minimization of the training errors. The sample data obtained from Moody's Industrial Manuals are employed to examine the classification ability of the proposed model. The results demonstrate that the proposed model performs well in comparison with the traditional multi-layer perceptron and some multivariate techniques.