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Extreme learning machine for missing data using multiple imputations
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DOI：10.1016/j.neucom.2015.03.108.png)
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摘要
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In the paper, we examine the general regression problem under the missing data scenario. In order to provide reliable estimates for the regression function (approximation), a novel methodology based on Gaussian Mixture Model and Extreme Learning Machine is developed. Gaussian Mixture Model is used to model the data distribution which is adapted to handle missing values, while Extreme Learning Machine enables to devise a multiple imputation strategy for final estimation. With multiple imputation and ensemble approach over many Extreme Learning Machines, final estimation is improved over the mean imputation performed only once to complete the data. The proposed methodology has longer running times compared to simple methods, but the overall increase in accuracy justifies this trade-off. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Extreme Learning Machine
Missing data
Multiple imputation
Gaussian mixture model
Mixture of Gaussians
Conditional distribution
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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