Abstract
Carrying out investigations into the relationships between the satisfaction in old-age security and its influence factors is of great significance for safeguarding social fairness and justice. As powerful statistical tools in machine learning, the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms are used to develop nonlinear estimation models for satisfaction in the old-age security in China. Five influence factors (educational background, educational satisfaction, satisfaction with family’s financial situation, overall life satisfaction, society overall evaluation) were used as the input features. A SVM model obtained in this paper has prediction accuracies of 78.0% for the training set and 77.5% for the test, and a BPNN model possesses prediction accuracies of 77.8% and 77.0% for the two tests. Obviously, the SVM is superior to the BPNN in predicting satisfaction of old-age security in China.
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Acknowledgements
This project was supported by the National Social Science Foundation (No. 16BZZ055). And the author gratefully wishes to express her thanks to Professor Yiyu Li from Xiangtan University for her careful guidance.
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Yu, N. Comparison of SVM and BPNN Estimation Models for Satisfaction in Old-Age Security. Ageing Int 46, 285–295 (2021). https://doi.org/10.1007/s12126-020-09388-5
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DOI: https://doi.org/10.1007/s12126-020-09388-5