首页|Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

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This paper deals with the reflectance estimation model issue to improve the estimation accuracy.We propose a model containing two core procedures:dimensionality reduction and model mining.First,the dimensionality reduction algorithm of hyperspectral data based on dependence degree (DRND-DD) is proposed to reduce the redundant hyperspectral band.DRND-DD solves the selection of suitable hyperspectral band via rough set theory.Furthermore,to improve the computation speed and accuracy of the model,based on DRND-DD,this paper proposes reflectance estimation model mining of leaf nitrogen concentration (LNC) for hyperspectral data by using hybrid gene expression programming (REMLNC-HGEP).Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis (PCA),singular value decomposition (SVD),a dimensionality reduction algorithm based on the positive region (AR-PR) and a dimensionality reduction algorithm based on a discernable matrix (AR-DM),and REMLNC-HGEP has low average time-consumption,high model mining success ratio and estimation accuracy.It was concluded that the REMLNC-HGEP performs better than the regression methods.

reflectance estimationdimensionality reductiongene expression programmingmodel mining

Lechan Yang、Zhihao Qin、Kun Wang、Song Deng

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International Institute for Earth System Science, Nanjing University, Nanjing 210093, China

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications,Nanjing 210023, China

Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

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This work was supported in part by the National Natural Science Foundation of ChinaThis work was supported in part by the National Natural Science Foundation of ChinaThis work was supported in part by the National Natural Science Foundation of ChinaNSF of Jiangsu ProvinceNUPTOpen research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (NUPT),Ministry of EducationQinlan Project of Jiangsu Province and the General Project of National Natural Science Found of China

11&zd1675150708461572262BK20141427NY214097NYKL20150741471300

2017

中国通信(英文版)

中国通信(英文版)

CSTPCDCSCDSCI
影响因子：0.463
ISSN：1673-5447
年,卷(期)：2017.14(1)
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