Title :
County Innovation System Efficiency Prediction Based on Support Vector Machine: Evidence from China
Author :
Zhao, Jing ; Dang, Xinghua
Author_Institution :
Sch. of Bus. Adm., Xi´´an Univ. of Technol., Xi´´an, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
Innovation system efficiency analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capacity for country. According to the county innovation system data which is large scale and imbalance, this paper presented a support vector machine model to predict county innovation system efficiency. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding county innovation system efficiency prediction for 83 Chinese counties. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county innovation system efficiency prediction.
Keywords :
data mining; economics; innovation management; support vector machines; China; artificial neural network; county innovation system efficiency prediction; data mining technology; decision tree; innovation system efficiency analysis; logistic regression; naive Bayesian classifier; regional innovation systems development; support vector machine model; Artificial neural networks; Bayesian methods; Decision trees; Large-scale systems; Logistics; Predictive models; Regression tree analysis; Support vector machine classification; Support vector machines; Technological innovation; Chinese county; county innovation system; prediction; support vector machine;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.96
