基于归一化互信息的FCBF特征选择算法
发布时间：2018-09-11 12:56
【摘要】：针对高维数据中不相关特征、冗余特征等导致的分类任务计算量大、分类正确率低等问题,提出了一种基于归一化互信息的相关性快速过滤特征选择(FCBF-NMI)算法.该算法采用归一化互信息代替对称不确定性作为FCBF算法的相关性评价标准,进行特征与类别、特征与特征的相关性分析,删除不相关特征及冗余特征以获得最优特征子集.实验结果表明:FCBF-NMI算法得到的最优特征子集更合理,平均分类正确率为89.68%,所用时间平均低至2.64s.
[Abstract]:A fast feature selection algorithm based on normalized mutual information (FCBF-NMI) is proposed to solve the problems of large computation and low classification accuracy caused by irrelevant features and redundant features in high dimensional data. In this algorithm, normalized mutual information is used instead of symmetric uncertainty as the criterion for evaluating the correlation of FCBF algorithm. The correlation analysis of features and categories, features and features is carried out, and irrelevant features and redundant features are deleted to obtain the optimal feature subset. The experimental results show that the optimal feature subset obtained by the FCBF-NMI algorithm is more reasonable, the average classification accuracy is 89.68 and the average time used is as low as 2.64 s.
【作者单位】： 兰州理工大学计算机与通信学院;
【基金】：国家自然科学基金资助项目(61363078) 甘肃省青年科技基金资助项目(148RJYA001)
【分类号】：TP301.6
，
本文编号：2236747
[Abstract]:A fast feature selection algorithm based on normalized mutual information (FCBF-NMI) is proposed to solve the problems of large computation and low classification accuracy caused by irrelevant features and redundant features in high dimensional data. In this algorithm, normalized mutual information is used instead of symmetric uncertainty as the criterion for evaluating the correlation of FCBF algorithm. The correlation analysis of features and categories, features and features is carried out, and irrelevant features and redundant features are deleted to obtain the optimal feature subset. The experimental results show that the optimal feature subset obtained by the FCBF-NMI algorithm is more reasonable, the average classification accuracy is 89.68 and the average time used is as low as 2.64 s.
【作者单位】： 兰州理工大学计算机与通信学院;
【基金】：国家自然科学基金资助项目(61363078) 甘肃省青年科技基金资助项目(148RJYA001)
【分类号】：TP301.6
，
本文编号：2236747
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