Feature assessment and ranking for classification with nonlinear sparse representation and approximate dependence analysis

2019
Feature selection has received significant attention in knowledge management and decision support systems in the past decades. In this study, kernel-based sparse representation and feature dependence analysis are integrated into a feature assessment and ranking framework. The proposed method utilizes the advantages of the kernel-based sparse representation technique and of the information theoretic metric to iteratively obtain the salient feature cluster. Then, a novel approximate dependence analysis is applied to further maintain complementarity while eliminating redundancy among the features selected by nonlinear orthogonal matching pursuit (NOMP). This can effectively prevent the significant bias caused by the pairwise correlation analysis for a large-scale feature set. To illustrate the effectiveness of the proposed method, classification experiments are conducted with three representative classifiers, on nine well-known datasets. The experimental results show the superiority of the proposed method compared with the representative information theoretic and model-based methods in classification for data-driven decision support systems.
DECISION SUPPORT SYSTEMS
卷号:122
ISSN:0167-9236
收录类型
SSCI
发表日期
2019
学科领域
循证管理学
国家
中国
语种
英语
DOI
10.1016/j.dss.2019.05.004
其他关键词
FEATURE-SELECTION; OPTIMIZATION; INFORMATION; RELEVANCE; DIAGNOSIS; FRAMEWORK
EISSN
1873-5797
资助机构
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71702066, 71802192, 61703319, 71772077]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M612856]; Humanity and Social Science Youth foundation of Ministry of Education of China [18YJC630137]; National Key R&D Program of China [2017YEB0102500]
资助信息
We would like to thank the associate editor and three anonymous reviewers for their constructive comments and suggestions. This study was supported in part by the National Natural Science Foundation of China under Grants71702066,71802192,61703319, and71772077, in part by China Postdoctoral Science Foundation under Grant2017M612856, in part by Humanity and Social Science Youth foundation of Ministry of Education of China under Grant18YJC630137, and in part by National Key R&D Program of China under Grant2017YEB0102500.
被引频次(WOS)
3
被引更新日期
2022-01
来源机构
Jinan University China University of Geosciences Wuhan University of Technology Pennsylvania Commonwealth System of Higher Education (PCSHE) University of Pittsburgh
关键词
Feature selection Dimensionality reduction Classification Sparse representation Dependence analysis