Title :
City Innovative Capability Prediction Based on Support Vector Machine: Taking Thirteen Chinese Cities as the Example
Author :
Zhao Jing ; Guo Hai-xing
Author_Institution :
Sch. of Econ. & Manage., Xi´an Univ. of Technol., Xi´an, China
Abstract :
City innovative capability analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capability for country. According to the city innovative capability data which is large scale and imbalance, this paper presented a support vector machine model to predict city innovative capability. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding city innovative capability prediction for thirteen Chinese cities. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for city innovative capability classification and prediction.
Keywords :
Bayes methods; decision trees; innovation management; neural nets; pattern classification; regression analysis; support vector machines; town and country planning; Chinese cities; artificial neural network; city innovative capability prediction; decision tree; logistic regression; naive Bayesian classifier; regional innovation systems development; support vector machine:; Accuracy; Artificial neural networks; Cities and towns; Decision trees; Kernel; Predictive models; Support vector machines;
Conference_Titel :
Management and Service Science (MASS), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5325-2
Electronic_ISBN :
978-1-4244-5326-9
DOI :
10.1109/ICMSS.2010.5576959
