NDLI: Application of GA-SVM prediction in county independent innovation ability: Taking Guanzhong urban agglomeration as the example

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Application of GA-SVM prediction in county independent innovation ability: Taking Guanzhong urban agglomeration as the example

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Volume 1 - 2010

Application of GA-SVM prediction in county independent innovation ability: Taking Guanzhong urban agglomeration as the example

Content Provider	IEEE Xplore Digital Library
Author	Zhao Jing Dang Xing-hua
Copyright Year	2010
Description	Author affiliation: School of Economics and Management, Xi'an University of Technology, XUT, China (Zhao Jing; Dang Xing-hua)
Abstract	County independent innovation ability analysis and prediction play an important role in county economic development and improve benefit of national independent innovation system. According to the county independent innovation ability data which is large scale and imbalance, this paper presented a support vector machine (SVM) model to predict county independent innovation ability. In order to improve the discrimination precision of SVM in prediction of county independent innovation ability, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The proposed GA-SVM method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding county independent innovation ability prediction for Guanzhong urban agglomeration. The result shows that the improved SVM has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county independent innovation ability classification and prediction.
File Size	286331
File Format	PDF
ISBN	9781424480333
e-ISBN	9781424480357
DOI	10.1109/ICEIT.2010.5607572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Biological system modeling County independent innovation ability Genetic algorithm Prediction Support vector machine Kernel Genetic algorithms
Content Type	Text
Resource Type	Article

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