A novel hybrid algorithm for feature selection

被引:0
作者
Yuefeng Zheng
Ying Li
Gang Wang
Yupeng Chen
Qian Xu
Jiahao Fan
Xueting Cui
机构
[1] Jilin University,College of Computer Science and Technology
[2] Jilin University,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education
[3] BODA College of Jilin Normal University,undefined
来源
Personal and Ubiquitous Computing | 2018年 / 22卷
关键词
Cuckoo search algorithm; Classification; Dimensionality reduction; Feature selection; Maximum Spearman and minimum covariance;
D O I
暂无
中图分类号
学科分类号
摘要
Feature selection is an important filtering method for data analysis, pattern classification, data mining, and so on. Feature selection reduces the number of features by removing irrelevant and redundant data. In this paper, we propose a hybrid filter–wrapper feature subset selection algorithm called the maximum Spearman minimum covariance cuckoo search (MSMCCS). First, based on Spearman and covariance, a filter algorithm is proposed called maximum Spearman minimum covariance (MSMC). Second, three parameters are proposed in MSMC to adjust the weights of the correlation and redundancy, improve the relevance of feature subsets, and reduce the redundancy. Third, in the improved cuckoo search algorithm, a weighted combination strategy is used to select candidate feature subsets, a crossover mutation concept is used to adjust the candidate feature subsets, and finally, the filtered features are selected into optimal feature subsets. Therefore, the MSMCCS combines the efficiency of filters with the greater accuracy of wrappers. Experimental results on eight common data sets from the University of California at Irvine Machine Learning Repository showed that the MSMCCS algorithm had better classification accuracy than the seven wrapper methods, the one filter method, and the two hybrid methods. Furthermore, the proposed algorithm achieved preferable performance on the Wilcoxon signed-rank test and the sensitivity–specificity test.
引用
收藏
页码:971 / 985
页数:14
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