An optimized classification algorithm by BP neural network based on PLS and HCA

doi:10.1007/s10489-014-0618-x

	An optimized classification algorithm by BP neural network based on PLS and HCA
	Jia, Weikuan 1; Zhao, Dean 1; Shen, Tian 1; Ding, Shifei 2,3; Zhao, Yuyan 1,4; Hu, Chanli 1
	2015-07-01
发表期刊	APPLIED INTELLIGENCE
ISSN	0924-669X
卷号	43 期号:1 页码:176-191
摘要	Due to some correlative or repetitive factors between features or samples with high dimension and large amount of sample data, when traditional back-propagation (BP) neural network is used to solve this classification problem, it will present a series of problems such as network structural redundancy, low learning efficiency, occupation of storage space, consumption of computing time, and so on. All of these problems will restrict the operating efficiency and classification precision of neural network. To avoid them, partial least squares (PLS) algorithm is used to reduce the feature dimension of original data into low-dimensional data as the input of BP neural network, so that it can simplify the structure and accelerate convergence, thus improving the training speed and operating efficiency. In order to improve the classification precision of BP neural network by using hierarchical cluster analysis (HCA), similar samples are put into a sub-class, and some different sub-classes can be obtained. For each sub-class, a different training session can be conducted to find a corresponding precision BP neural network model, and the simulation samples of different sub-classes can be recognized by the corresponding network model. In this paper, the theories of PLS and HCA are combined together with the property of BP neural network, and an optimized classification algorithm by BP neural network based on PLS and HCA (PLS-HCA-BP algorithm) is proposed. The new algorithm is aimed at improving the operating efficiency and classification precision so as to provide a more reliable and more convenient tool for complex pattern classification systems. Three experiments and comparisons with four other algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-HCA-BP algorithm, which is worthy of further promotion.
关键词	Partial least squares Hierarchical cluster analysis BP neural network PLS-HCA-BP classification algorithm
DOI	10.1007/s10489-014-0618-x
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[60875052] ; National Natural Science Foundation of China[61203014] ; National Natural Science Foundation of China[61379101] ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Major Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period[2011BAD20B06] ; Specialized Research Fund for the Doctoral Program of Higher Education of China[20133227110024] ; Ordinary University Graduate Student Research Innovation Projects of Jiangsu Province[KYLX 14_1062]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000355619500012
出版者	SPRINGER
引用统计	被引频次：67[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/2XEOYT63/9722
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, Dean
作者单位	1.Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Peoples R China 2.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221008, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China 4.Changzhou Coll Informat Technol, Changzhou 213164, Peoples R China
推荐引用方式 GB/T 7714	Jia, Weikuan,Zhao, Dean,Shen, Tian,et al. An optimized classification algorithm by BP neural network based on PLS and HCA[J]. APPLIED INTELLIGENCE,2015,43(1):176-191.
APA	Jia, Weikuan,Zhao, Dean,Shen, Tian,Ding, Shifei,Zhao, Yuyan,&Hu, Chanli.(2015).An optimized classification algorithm by BP neural network based on PLS and HCA.APPLIED INTELLIGENCE,43(1),176-191.
MLA	Jia, Weikuan,et al."An optimized classification algorithm by BP neural network based on PLS and HCA".APPLIED INTELLIGENCE 43.1(2015):176-191.