Extreme learning machine for missing data using multiple imputations

doi:10.1016/j.neucom.2015.03.108

Extreme learning machine for missing data using multiple imputations

2016-01-01

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DOI：10.1016/j.neucom.2015.03.108

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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.

Keywords:

Extreme Learning Machine

Missing data

Multiple imputation

Gaussian mixture model

Mixture of Gaussians

Conditional distribution

Extreme learning machine for missing data using multiple imputations

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