Coordinated development degree of county socio-economic system prediction based on support vector machine: Taking twenty-six Chinese counties as the example

Abstract

Coordinated development degree of county socio-economic system analysis and prediction play an important role in urban agglomeration coordinated development and improve benefit of regional coordinated development in China. According to the county socio-economic system data which is large scale and imbalance, this paper presented a support vector machine model to predict coordinated development degree of county socio-economic system. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county socio-economic system prediction for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for coordinated development degree of county socio-economic system classification and prediction.