A novel technique to prune variable selection ensembles

doi:10.1109/FSKD.2017.8393311

CORC > 兰州理工大学 > 兰州理工大学 > 经济管理学院

	A novel technique to prune variable selection ensembles
	Ren, Liang-Pin 1; Zhang, Chun-Xia 3; Xuan, Hai-Yan 2
	2018-06-21
会议日期	July 29, 2017 - July 31, 2017
会议地点	Guilin, Guangxi, China
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
页码	449-454
英文摘要	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.
会议录	ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
会议录出版者	Institute of Electrical and Electronics Engineers Inc.
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
资助项目	National Natural Science Foundations of China[11671317]
WOS研究方向	Computer Science
WOS记录号	WOS:000437355300073
内容类型	会议论文
源URL	[http://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.Lanzhou Univ Technol, Sch Econ & Management, Lanzhou 730050, Gansu, Peoples R China 3.Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, 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]. 见:. Guilin, Guangxi, China. July 29, 2017 - July 31, 2017.