Matching Degree Prediction of Region Industry-Population-Knowledge System Based on GA-SVM: County-Level Evidence from Guanzhong Urban Agglomeration in China

作者：Jing Zhao^*; Duo Huang; Wei Zhang

来源：Information-An International Interdisciplinary Journal, 2012, 15(11B): 4797-4802.

• County Industry-Population-Knowledge system
• Support vector machine
• Guanzhong urban agglomeration
• Genetic algorithm
• Prediction

County Industry-Population-Knowledge matching degree analysis and prediction play an important role in region economic development and improve the transformation of national economic growth pattern. According to the county Industry-Population-Knowledge composite system data which is large scale and imbalance, this paper presented a support vector machine (SVM) model to predict county Industry-Population-Knowledge matching 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 Industry-Population-Knowledge matching 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 Industry-Population-Knowledge matching degree classification and prediction.

• 出版日期2012-11
• 单位西安理工大学