HyperSurface classifiers ensemble for high dimensional data sets

	HyperSurface classifiers ensemble for high dimensional data sets
	Zhao, Xiu-Rong; He, Qing; Shi, Zhong-Zhi
	2006
发表期刊	ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1
ISSN	0302-9743
卷号	3971 页码:1299-1304
摘要	Based on Jordan Curve Theorem, a universal classification method called HyperSurface Classifier (HSC) has recently been proposed. Experimental results show that in three-dimensional space, this method works fairly well in both accuracy and efficiency even for large size data up to 10(7). However, what we really need is an algorithm that can deal with data not only of massive size but also of high dimensionality. In this paper, an approach based on the idea of classifiers ensemble by dimension dividing without dimension reduction for high dimensional data is proposed. The most important difference between HSC ensemble and the traditional ensemble is that the sub-datasets are obtained by dividing the features rather than by dividing the sample set. Experimental results show that this method has a preferable performance on high dimensional datasets.
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000238112000193
出版者	SPRINGER-VERLAG BERLIN
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/10700
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Xiu-Rong
作者单位	Chinese Acad Sci, Comp Technol Inst, Dept Intelligence Software, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Xiu-Rong,He, Qing,Shi, Zhong-Zhi. HyperSurface classifiers ensemble for high dimensional data sets[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1,2006,3971:1299-1304.
APA	Zhao, Xiu-Rong,He, Qing,&Shi, Zhong-Zhi.(2006).HyperSurface classifiers ensemble for high dimensional data sets.ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1,3971,1299-1304.
MLA	Zhao, Xiu-Rong,et al."HyperSurface classifiers ensemble for high dimensional data sets".ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1 3971(2006):1299-1304.