Extreme Learning Machine for Missing Data using Multiple Imputations

Abstract

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.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Conditional distribution; Extreme Learning Machine; Gaussian mixture model; Missing data; Mixture of Gaussians; Multiple imputation

International Standard Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

22 Jan 2016

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