面向高维数据PCA-ReliefF的EP模式分类算法

EP Pattern Classification Algorithm for High Dimensional Data Based on PCA-Relief F Computer Engineering and Applications

针对高维数据集,文中提出一种PREP(PCA-Relief F for EP)算法:首先采用PCA和Relief F算法实现特征降维;然后利用EP模式思想,构造精度更高、规模更小的EP模式分类器;最后利用标准数据集对文中的方法进行测试。实验结果表明,在对高维数据进行分类时,该方法构造的分类器在预测精度和运行时间上均有较大幅度的提升。

For high dimensional data sets,PREP（ PCA- Relief F for EP） algorithm is presented. Firstly,the feature dimension reduction is realized by using the PCA and Relief F algorithm. Then,higher precision and smaller EP classifier is constructed by using the EP model of ideological construction. Finally,the method of PREP is tested by using the standard data. The results show that structured classifier constructed by this method has a great improvement in the prediction accuracy and running time for the high dimensional data.