Regression in the presence missing data using ensemble methods

Mostafa M. Hassan, Amir F. Atiya, Neamat El-Gayar, Raafat El-Fouly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2007 International Joint Conference on Neural Networks
PublisherIEEE
Pages1261-1265
Number of pages5
ISBN (Print)9781424413799
DOIs
Publication statusPublished - 29 Oct 2007
Event2007 International Joint Conference on Neural Networks - Orlando, United States
Duration: 12 Aug 200717 Aug 2007

Conference

Conference2007 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2007
CountryUnited States
CityOrlando
Period12/08/0717/08/07

ASJC Scopus subject areas

  • Software

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