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| 一种神经网络分类器样本数据选择方法 | |
| 引用本文： | 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 |
| 作者姓名： | 周玉 朱安福 周林 钱旭 |
| 作者单位： | 1. 华北水利水电学院电力学院,河南郑州450011 中国矿业大学北京机电与信息工程学院,北京100083 2. 华北水利水电学院电力学院,河南郑州,450011 3. 清华大学信息技术研究院,北京,100084 4. 中国矿业大学北京机电与信息工程学院,北京,100083 |
| 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 |
| 摘 要： | 为了提高神经网络分类器的性能，提出一种基于阴影集的训练样本数据选择方法．在阴影集的基础上提出核数据和边界数据的概念．首先通过模糊c均值聚类（FCM）获取样本数据的最优模糊矩阵；然后诱导出相应的阴影集；样本数据结合阴影集构造核数据和边界数据；最后在核数据和边界数据中进行数据选择．利用该方法，结合Iris数据集分别对BP网络、LVQ网络和可拓神经网络（ENN）等分类器进行实验研究．结果表明：该方法能够保留典型的样本，减少训练样本数据的数量；利用该方法所选择的数据对神经网络分类器进行训练，保证了分类器的泛化能力，节约了训练时间，有效提高分类器的性能． |
| 关 键 词： | 神经网络 分类器 数据选择 阴影集 核数据 边界数据 |
Sample data selection method for neural network classifiers |
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| Zhou Yu,Zhu Anfu,Zhou Lin,Qian Xu. Sample data selection method for neural network classifiers[J]. JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE, 2012, 40(6): 39-43 | |
| Authors: | Zhou Yu Zhu Anfu Zhou Lin Qian Xu |
| Affiliation: | 1 School of Electric Power,North China University of Water Resources and Electric Power, Zhengzhou 450011,China;2 School of Mechanical Electronic and Information Engineering, China University of Mining and Technology,Beijing 100083,China;3 Research Institute of Information Technology,Tsinghua University,Beijing 100084,China) |
| 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. |
| Keywords: | neural networks classifiers data selection shadowed sets core data boundary data |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |