计算机应用与软件Issue(2)：51-54,4.DOI:10.3969/j.issn.1000-386x.2016.02.012
感染性腹泻周发病例数的 PCA-SVM 回归预测研究
RESEARCH ON PCA-SVM REGRESSIVE PREDICTION OF WEEKLY CASES OF INFECTIOUS DIARRHEA
摘要
Abstract
We proposed a regressive prediction method for the weekly cases number of infectious diarrhea using PCA-SVM,which effectively avoids some defects of the BP neural network model like local extremum,multicollinearity.With the weekly cases of infectious diarrhea in Shanghai from the year 2005 to 2008 being the samples,we built the PCA-SVM regressive model.First,we employed PCA to extract meteorological main principal factors from the statistical meteorological factors and removed the multicollinearity from the predictive factors,derived the explanatory variable of the final model.Secondly,we used SVMregression to build the predictive model for weekly cases number of infectious diarrhea in Shanghai.To illustrate the better prediction effect of the model,we compared it with BP neural network model in terms of fitting and prediction results.Numerical results showed that the MAPE and RMSE (0.2694 and 33.113 respectively) predicted by PCA-SVMregression model were all less than those of BP neural network model (0.3745 and 49.909 respectively).Meanwhile, its determination parameter R2 (0.9089)was further approaching 1 than that of BP neural network (0.8590).As a result,it is demonstrated in this paper that the PCA-SVM regressive model has higher prediction accuracy and stronger generalisation capability in predicting weekly cases number of infectious diarrhea,the prediction of the model is reliable on the weekly cases number of the disease,and has better practical value in publicising the diarrhea prediction.关键词
PCA/SVM回归/感染性腹泻/气象资料Key words
PCA/SVMregression/Infectious diarrhea/Meteorological data分类
信息技术与安全科学引用本文复制引用
霍静,王永明,顾君忠..感染性腹泻周发病例数的 PCA-SVM 回归预测研究[J].计算机应用与软件,2016,(2):51-54,4.基金项目
上海市国际科技合作基金项目（13430710100）；甘肃省科技计划资助项目（1506RJZE115）；甘肃省高等学校科研项目（2015B-104）。 （）
