Analysis and Prediction of Logistics Enterprise Competitiveness by Using a Real GA-Based Support Vector Machine

被引：0
|
作者：
Ding, Ning [1 ]
Li, Hanqing [2 ]
Wang, Hongqi [2 ]
机构：
[1] China Agr Univ, Sch Human & Dev, Beijing 100083, Peoples R China
[2] Beijing Jiaotong Univ, Sch Econ, Beijing 100044, Peoples R China
来源：
PROCEEDINGS OF 2ND CONFERENCE ON LOGISTICS, INFORMATICS AND SERVICE SCIENCE (LISS 2012), VOLS 1 AND 2 | 2013年
关键词：
SVM; Logistics enterprise competitiveness; GA; Forecast;
D O I：
10.1007/978-3-642-32054-5_40
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This research is aimed at establishing the forecast and analysis diagnosis models for competitiveness of logistics enterprise through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. The result of the proposed GA-SVM can satisfy a predicted accuracy of up to 95.56% for all the tested logistics enterprise competitive data. Notably, there are only 12 influential feature included in the proposed model, while the six features are ordinary and easily accessible from National Bureau of Statistics. The proposed GA-SVM is available for objective description forecast and evaluation of a logistics enterprise competitiveness and stability of steady development.
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页数：6
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