Combination Forecasting Model Based on the Dynamic Classifier Ensemble Selection and GM(2,1)

• 1、Department Of Business Administration,Liaoning Technical University,Huludao,125105

Abstract：The prediction of less sample data or missing data often exist in the process of emergencies, classifier integration technology based on lack of established model of DCESM to compensate for the missing data, combined with the grey system model of GM(2,1), dynamic combination. The model of DCESM can make full use of all the information contained in the sample data set, and select the appropriate subset of classifiers to classify the test samples, thus forming a standardized prediction sample. Grey system GM(2,1) is a new method to study the uncertainty problem of less data and poor information, which is suitable for long-term prediction. 11 sets of statistical data are used as training samples to train the model, and the relative error is used to test the model. Finally, 5 main indexes are selected, and the forecasting model is used to analyze the lightning disaster index of Guangdong Province in 2015. The results show that the modCombination Forecasting Model Based on the Dynamic Classifier Ensemble Selection and GM(2,1)el accuracy reaches 95% when the misclassification rate is 11% and the model error is less than 20%,rate is 11% and the model error is less than 20%,actual situation.

Keywords： Safety Engineering Sciences prediction model missing data the model of classifierDCESM grey system GM(2,1)