C4.5: programs for machine learning
C4.5: programs for machine learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Applied Intelligence
Genetic algorithms for generation of class boundaries
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
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This paper proposes a coevolutionary classification method to discover classifiers for multidimensional pattern classification problems with continuous features. The classification problems may be decomposed into two sub-problems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two sub-problems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by combining a genetic algorithm and a local adaptation algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with well-known data sets from the UCI machine-learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.