Coordinated Development Degree of County Socio-Economic System Prediction Based on GA-SVM

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 (SVM) model to predict coordinated development degree of county socio-economic system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. 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.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[3]  H. Pomares,et al.  A heuristic method for parameter selection in LS-SVM: Application to time series prediction , 2011 .

[4]  P. Vega,et al.  Neural predictive control. Application to a highly non-linear system , 1999 .

[5]  Hongwei Liu,et al.  Variant of Gaussian kernel and parameter setting method for nonlinear SVM , 2009, Neurocomputing.

[6]  Sung Jin Yoo,et al.  Stable Predictive Control of Chaotic Systems Using Self-Recurrent Wavelet Neural Network , 2005 .

[7]  Eslam Pourbasheer,et al.  Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. , 2009, European journal of medicinal chemistry.

[8]  João Francisco Valiati,et al.  Document-level sentiment classification: An empirical comparison between SVM and ANN , 2013, Expert Syst. Appl..

[9]  Yansui Liu,et al.  Spatio-temporal change of urban–rural equalized development patterns in China and its driving factors , 2013 .

[10]  Tao Cheng,et al.  Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification , 2012, Comput. Environ. Urban Syst..

[11]  Yoon Ho Choi,et al.  Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems , 2004 .

[12]  Saeed Jalili,et al.  PSSP with dynamic weighted kernel fusion based on SVM-PHGS , 2012, Knowl. Based Syst..

[13]  Hongping Yuan,et al.  Critical indicators for assessing the contribution of infrastructure projects to coordinated urban–rural development in China , 2012 .

[14]  Mohammad Reza Nikoo,et al.  Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction , 2011 .

[15]  Jian Zhang,et al.  Novel support vector regression for structural system identification , 2007 .

[16]  Siyang Zhang,et al.  A novel hybrid KPCA and SVM with GA model for intrusion detection , 2014, Appl. Soft Comput..