Applied multivariate techniques
Applied multivariate techniques
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
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
Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Fuzzy Data Mining: Effect of Fuzzy Discretization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Fuzzy Classifier Design
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
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Is standard multivariate analysis sufficient in clinical and epidemiological studies?
Journal of Biomedical Informatics
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The aim of this paper is to develop a fuzzy classifier form the point of view of a fuzzy information retrieval system. The genetic algorithm is employed to find useful fuzzy concepts with high classification performance for classification problems; then, each of classes and patterns can be represented by a fuzzy set of useful fuzzy concepts. Each of fuzzy concepts is linguistically interpreted and the corresponding membership functions remain fixed during the evolution. A pattern can be categorized into one class if there exists a maximum degree of similarity between them. For not distorting the usefulness of the proposed classifier for high-dimensional problems, the principal component analysis is incorporated into the proposed classifier to reduce dimensions. The generalization ability of the proposed classifier is examined by performing computer simulations on some well-known data sets, such as the breast cancer data and the wine classification data. The results demonstrate that the proposed classifier works well in comparison with other classification methods.