Application of GA-SVM Prediction in Region Coordinated Development of Economy-Resource-Environment System: County-Level Evidence from China Guanzhong Urban Agglomeration

摘要

County Economy-Resource-Environment coordinated development analysis and prediction play an important role in region economic development and improve the transformation of national economic growth pattern. According to the county Economy-Resource-Environment composite system data which is large scale and imbalance, this paper presented a support vector machine (SVM) model to predict county Economy-Resource-Environment coordinated development degree. In order to improve the discrimination precision of SVM in prediction, 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 Economy-Resource-Environment coordinated development degree prediction for China 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 Economy-Resource-Environment coordinated development degree classification and prediction.