A Fast Feature Selection Framework and Method

被引：0

作者：

Qiu L.-K. ^{[1
]}

Liu J. ^{[2
]}

Sun Z.-W. ^{[3
]}

Zhao Y.-F. ^{[4
]}

机构：

[1] Information Management Department, Shandong Foreign Trade Vocational College, Qingdao

[2] Science and Information College, Qingdao Agricultural University, Qingdao

[3] School of Information and Control Engineering, Qingdao University of Technology, Qingdao

[4] Comprehensive Planning Office, Shandong Qingdao Tobacco Copany Limited, Qingdao

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2019年 / 42卷 / 03期

关键词：

Correlation coefficient; Feature selection; Filter; Hybrid; Performance prediction; Wrapper;

D O I：

10.13190/j.jbupt.2018-151

中图分类号：

学科分类号：

摘要：

Aiming at the imbalance between accuracy and computational efficiency in feature selection, a fast feature selection framework (FFFS) is proposed. Based on this framework, a fast feature selection algorithm, MRMR-SFS, is proposed. The minimum redundancy maximum relevance (MRMR) method is used to select the candidate features, and sequential forward selection (SFS) method is used to verify the performance of the candidate features as well. It improves the calculation efficiency by limiting the number of iterations. Comparison experiments with the MRMR, SFS and a filter-dominating hybrid sequential floating forward selection algorithms demonstrate that MRMR-SFS can balance the accuracy and computational efficiency well. © 2019, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：127 / 132

页数：5

共 8 条

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