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Received:October 14, 2010 Revised:November 20, 2010
Received:October 14, 2010 Revised:November 20, 2010
中文摘要: 高维且不独立的样本特征集使分类的质量降低，提出特征权值计算方法，并用于特征加权及特征选择，根据特征的相似性度量函数计算特征的权重，并根据权重排序去除重要性差的特征，用于解决高维样本集的特征降维问题，特征选择结果与主成份分析结果一致。并建立基于保留特征加权的云分类模型，应用于iris 数据集和复杂矿石图像的分类，效果良好。
Abstract:A cloud classifier based on swarm particle optimization (PSO) is presented, and used in the classification for
multi-dimension object. The digital characteristic of cloud model is expected value Ex、entropy and super entropy He,
the membership to which every attribute data of classified object belongs to its attribute set center is presents by 1-D
cloud model. The digital characteristic of 1-D cloud model is optimized by swarm particle optimization (SPO). The
swarm particle optimization cloud classifier (SPOCC) is built from every attribute cloud model, and used in the
classification of iris data set, the experiment result is very well.
文章编号： 中图分类号： 文献标志码：
基金项目:
| Author Name | Affiliation |
| QIN Cai-Yun | College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China |
| Author Name | Affiliation |
| QIN Cai-Yun | College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China |
引用文本：
秦彩云.云模型用于特征加权及降维的算法.计算机系统应用,2011,20(6):196-199,168
QIN Cai-Yun.Weighting and Degrading Dimension Algorithm Based on Cloud Model.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):196-199,168
秦彩云.云模型用于特征加权及降维的算法.计算机系统应用,2011,20(6):196-199,168
QIN Cai-Yun.Weighting and Degrading Dimension Algorithm Based on Cloud Model.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):196-199,168
