Fast instance selection for speeding up support vector machines

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Fast instance selection for speeding up support vector machines
	Chen, Jingnian 1; Zhang, Caiming 2; Xue, Xiaoping 3; Liu, Cheng-Lin4
发表期刊	KNOWLEDGE-BASED SYSTEMS
	2013-06-01
卷号	45 页码:1-7
文章类型	Article
摘要	Support vector machine (SVM) has shown prominent performance for binary classification. How to effectively apply it to massive datasets with large number of classes and instances is still a serious challenge. Instance selection methods have been proposed and shown significant efficacy for reducing the training complexity of SVM, but more or less trade off the generalization performance. This paper presents an instance selection method especially for multi-class problems. With cluster centers of positive class as reference points instances are selected for each one-versus-rest SVM model. The purpose of clustering here is to improve the efficiency of instance selection, other than to select instances directly from clusters as previous methods did. Experiments on a wide variety of datasets demonstrate that the proposed method selects fewer instances than most competitive algorithms and keeps the highest classification accuracy on most datasets. Additionally, experimental results show that this method also performs superiorly for binary problems. (C) 2013 Elsevier B.V. All rights reserved.
关键词	Svm Classification Multi-class Instance Selection Clustering
WOS标题词	Science & Technology ; Technology
关键词[WOS]	LARGE DATA SETS ; CLASSIFIERS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000318384200001
引用统计	被引频次：65[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3080
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.Shandong Univ Finance & Econ, Dept Informat & Comp Sci, Jinan 250014, Peoples R China 2.Shandong Univ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China 3.Tongji Univ, Sch Elect & Informat, Shanghai 201804, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jingnian,Zhang, Caiming,Xue, Xiaoping,et al. Fast instance selection for speeding up support vector machines[J]. KNOWLEDGE-BASED SYSTEMS,2013,45:1-7.
APA	Chen, Jingnian,Zhang, Caiming,Xue, Xiaoping,&Liu, Cheng-Lin.(2013).Fast instance selection for speeding up support vector machines.KNOWLEDGE-BASED SYSTEMS,45,1-7.
MLA	Chen, Jingnian,et al."Fast instance selection for speeding up support vector machines".KNOWLEDGE-BASED SYSTEMS 45(2013):1-7.