Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Digital neural networks
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
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
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computers and Operations Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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For the outranking relation theory, the ELECTRE methods are one of the most extensively used outranking methods. To measure the degree of agreement and the degree of disagreement of the proposition ''one alternative outranks another alternative'', the concordance and discordance relations are usually associated with the outranking relation. Instead of the traditional single-layer perceptron (SLP) developed according to the multiple-attribute utility theory, this paper contributes to develop a novel ELECTRE-based SLP for multicriteria classification problems based on the ELECTRE methods involving pairwise comparisons among patterns. A genetic-algorithm-based method is then designed to determine connection weights. A real-world data set involving bankruptcy analysis obtained from Moody's Industrial Manuals is employed to examine the classification performance of the proposed ELECTRE-based model. The results demonstrate that the proposed model performs well compared to an arsenal of well-known classification methods involving quantitative disciplines of statistics and machine learning.