A novel technique to prune variable selection ensembles

doi:10.1109/FSKD.2017.8393311

IR > 经济管理学院

	A novel technique to prune variable selection ensembles
	Ren, Liang-Pin 1; Zhang, Chun-Xia 2; Xuan, Hai-Yan3
	2018-06-21
会议名称	13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
会议录名称	ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
页码	449-454
会议日期	July 29, 2017 - July 31, 2017
会议地点	Guilin, Guangxi, China
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	In ensemble learning field, it has been proven that selective ensemble learning (i.e., only fusing some instead of all ensemble members) can further improve the prediction ability of an ensemble machine. In this paper, we apply it in another framework, that is, variable selection problems in linear regression models. Under this situation, the main goal is to accurately detect the variables which have real influence on the response. As for the existing algorithms to construct a variable selection ensemble, they generally combine all the members to create an importance measure for each variable. In this paper, we propose to insert an additional pruning phase into a state-of-the-art algorithm ST2E [14]. By defining a reference vector, we sort the members generated by ST2E according to the angle between each of them and the reference vector. Then, a subensemble is obtained by only keeping some members ranked ahead. We investigated the performance of the proposed method on several simulated data sets. The experimental results show that it performs better than the original full ensemble as well as several other rivals. © 2017 IEEE.
关键词	Regression analysis Importance measure Linear regression models Reference vectors Selective ensembles Simulated datasets State-of-the-art algorithms Variable selection Variable selection problems
DOI	10.1109/FSKD.2017.8393311
收录类别	EI
资助项目	National Natural Science Foundations of China[11671317]
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000437355300073
EI入藏号	20183005590246
EI主题词	Fuzzy systems
来源库	Compendex
分类代码	922.2 Mathematical Statistics - 961 Systems Science
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/118158
专题	经济管理学院
通讯作者	Ren, Liang-Pin
作者单位	1.Zhengzhou Univ, Sch Software & Appl Technol, Zhengzhou 450002, Henan, Peoples R China 2.Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, Peoples R China 3.Lanzhou Univ Technol, Sch Econ & Management, Lanzhou 730050, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Ren, Liang-Pin,Zhang, Chun-Xia,Xuan, Hai-Yan. A novel technique to prune variable selection ensembles[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2018:449-454.