Extreme learning machine for missing data using multiple imputations

Dušan Sovilj, Emil Eirola, Yoan Miche, Kaj-Mikael Björk, Rui Nian, Anton Akusok, Amaury Lendasse

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

65 Citeringar (Scopus)

Sammanfattning

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.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftNeurocomputing
Volym174, Part A
UtgåvaJanuary
Sidor (från-till)220-231
Antal sidor12
ISSN0925-2312
DOI
StatusPublicerad - 22.01.2016
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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