Sample data selection method for neural network classifiers

In order to improve the performance of neural network classifiers (NNCs), a novel sample data selection method based on shadowed sets was proposed. On the basis of shadowed sets, core data and boundary data were established. First, the optimal fuzzy matrix of sample data was acquired by using FCM. Then, corresponding shadowed sets were induced. On the foundation of sample data and shadowed sets, core data and boundary data could be formed. Finally, the sample data of NNCs could be selected effectively from core data and boundary data. Applying this method and Iris data, experiments for BP neural network, LVQ neural network and extension neural network (ENN) are conducted. Experimental results show that the proposed method can keep typical sample data and reduce the number of training sample data. And with selected sample to train NNCs data can save training time, guarantee generalization ability, and effectively achieve a better performance.