The feature extraction procedure for pattern recognition with learning using genetic algorithm
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
The bayes-optimal feature extraction procedure for pattern recognition using genetic algorithm
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.