云模型用于特征加权及降维的算法

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。

Weighting and Degrading Dimension Algorithm Based on Cloud Model

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.