Regression in the Presence Missing Data Using Ensemble Methods
Regression in the Presence Missing Data Using Ensemble Methods
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DOI：
10.1109/ijcnn.2007.4371139
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
Mostafa M. Hassan;A. Atiya;N. E. Gayar;R. El-Fouly
中科院分区：
文献类型：
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作者：
Mostafa M. Hassan;A. Atiya;N. E. Gayar;R. El-Fouly
我们考虑丢失数据的问题，并开发集成网络模型来处理丢失的数据。所提出的方法基于利用缺失记录的固有不确定性为集成网络生成不同的训练集。所提出的方法基于使用概率密度生成缺失值。我们多次重复这个过程，从而创建几个完整的数据集。针对每个数据集训练一个网络，从而获得网络集合。提出了几种变体，包括单变量方法和多变量方法，它们的不同之处在于生成缺失值的方式。仿真结果证实了所提出的方法相对于传统方法的总体优越性。
We consider the problem of missing data, and develop ensemble-network models for handling the missing data. The proposed method is based on utilizing the inherent uncertainty of the missing records in generating diverse training sets for the ensemble's networks. The proposed method is based on generating the missing values using their probability density. We repeat this procedure many time thereby creating several complete data sets. A network is trained for each of these data sets, therefore obtaining an ensemble of networks. Several variants are proposed, including the univariate approach and the multivariate approach, which differ in the way missing values are generated. Simulation results confirm the general superiority of the proposed methods compared to the conventional approaches.