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基于神经网络集成的失业预警方法
引用本文:李宏,李建武,宋玉龙.基于神经网络集成的失业预警方法[J].经济与管理研究,2012(1):89-94.
作者姓名:李宏  李建武  宋玉龙
作者单位:1. 中国人民大学劳动人事学院,国家人力资源和社会保障部劳动科学研究所,北京,100872
2. 北京理工大学计算机学院智能信息技术北京市重点实验室,北京,100081
3. 北京理工大学计算机学院智能信息技术北京市重点实验室
基金项目:国家科技部软科学研究计划项目,北京市自然科学基金,人力资源和社会保障部失业保险司课题,广东省人力资源和社会保障厅委托项目
摘    要:提出采用神经网络集成技术对中国失业预警系统进行建模,以克服当前失业预警系统建模中存在的小样本、高维度、非线性、噪音数据等难题。采用BP神经网络回归模型对失业率进行预测;基于两种集成技术Bagging与AdaBoost对多个神经网络进行集成,以获得比单个预测模型更好的精度与稳定性;最后基于广东省的社会经济调查数据进行了实证分析,实验结果表明:在对失业率的预测上,Bagging集成方法的预测效果优于Adaboost集成方法,也优于单个最好的神经网络模型。

关 键 词:失业预警  神经网络集成  Bagging  AdaBoost

Unemployment Early -Warning Based on Neural Network Ensembles
LI Hong,LI Jian-wu,SONG Yu-long.Unemployment Early -Warning Based on Neural Network Ensembles[J].Research on Economics and Management,2012(1):89-94.
Authors:LI Hong  LI Jian-wu  SONG Yu-long
Institution:LI Hong(1,2),LI Jian - wu3,SONG Yu - long3 (1.School of Labor and Human Resources,Renmin University of China,Beijing 100872; 2.Institute of Labor Science,Ministry of Human Resources and Social Security of the People’s Republic of China,Beijing 100873;3.School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100871)
Abstract:This paper proposes to apply neural network ensembles to unemployment early -warning systems modeling in order to overcome effectively some difficult problems,such as small samples,high dimensions,nonlinearity,noisy data.BP neural networks are utilized as individual forecasters to evaluate unemployment rates,and two ensemble methods,Bagging and Ada-Boost, are used to combine forecasting results of many individual neural networks to obtain better forecasting effectiveness.Experimental results based on social and economic investigation data from Guangdong province show that Bagging produced better effectiveness on forecasting unemployment rates than both AdaBoost and the best individual neural network.
Keywords:Unemployment Early - warning  Neural Network Ensembles  Bagging  AdaBoost
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