华中科技大学学报：自然科学版2012，Vol.40Issue(6)：39-43,5.
一种神经网络分类器样本数据选择方法
Sample data selection method for neural network classifiers
摘要
Abstract
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.关键词
神经网络/分类器/数据选择/阴影集/核数据/边界数据Key words
neural networks/classifiers/data selection/shadowed sets/core data/boundary data分类
计算机与自动化引用本文复制引用
周玉,朱安福,周林,钱旭..一种神经网络分类器样本数据选择方法[J].华中科技大学学报：自然科学版,2012,40(6):39-43,5.基金项目
国家自然科学基金资助项目 （）
教育部科学技术研究重点资助项目 （）
华北水利水电学院高层次人才科研启动基金资助项目（201117）. （）
