NOVEL ENSEMBLE TECHNIQUES FOR REGRESSION WITH MISSING DATA

Mostafa M. Hassan (), Amir F. Atiya (), Neamat El Gayar () and Raafat El-Fouly ()
Additional contact information
Mostafa M. Hassan: Computer Engineering, Cairo University, Giza, Egypt
Amir F. Atiya: Computer Engineering, Cairo University, Giza, Egypt
Neamat El Gayar: Faculty of Computer and Information Technology, Cairo University, Giza, Egypt
Raafat El-Fouly: Computer Engineering, Cairo University, Giza, Egypt

New Mathematics and Natural Computation (NMNC), 2009, vol. 05, issue 03, 635-652

Abstract: In this paper, we consider the problem of missing data, and develop an ensemble-network model 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. Specifically we generate the missing values using their probability distribution function. We repeat this procedure many times thereby creating a number of complete data sets. A network is trained for each of these data sets, thereby obtaining an ensemble of networks. Several variants are proposed, and we show analytically that one of these variants is superior to the conventional mean-substitution approach for the limit of large training set. Simulation results confirm the general superiority of the proposed methods compared to the conventional approaches.

Keywords: Missing values; missing value imputation; ensemble networks; regression (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S1793005709001477
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:nmncxx:v:05:y:2009:i:03:n:s1793005709001477

Ordering information: This journal article can be ordered from

DOI: 10.1142/S1793005709001477

Access Statistics for this article

New Mathematics and Natural Computation (NMNC) is currently edited by Paul P Wang

More articles in New Mathematics and Natural Computation (NMNC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().