C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Applied Intelligence
Pattern Recognition Letters
Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization
Information Sciences: an International Journal
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
The application of neural networks to the papermaking industry
IEEE Transactions on Neural Networks
Implementation of a scalable decision forest model based on information theory
Expert Systems with Applications: An International Journal
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Deflection yoke (DY) is one of the core components of a cathode ray tube (CRT) in a computer monitor or a television that determines the image quality. Once a DY anomaly is found from beam patterns on a display in the production line of CRTs, the remedy process should be performed through three steps: identifying misconvergence types from the anomalous display pattern, adjusting manufacturing process parameters, and fine tuning. This study focuses on discovering a classifier for the identification of DY misconvergence patterns by applying a coevolutionary classification method. The DY misconvergence classification problems may be decomposed into two subproblems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two subproblems, 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 using a genetic 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 real-field data acquired from the production line of a computer monitor manufacturer in Korea, showing superior performance to other methods such as k-nearest neighbors, decision trees, and neural networks.