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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4134

Title: A dynamic programming approach to missing data estimation using neural networks
Authors: Nelwamondo, FV
Golding, D
Marwala, T
Keywords: Missing data techniques
Genetic algorithms
Bellman's Equation
Dynamic programming
Neural networks
Data imputation
Issue Date: 2013
Publisher: Elsevier
Citation: Nelwamondo, FV, Golding, D and Marwala, T. 2013. Dynamic programming approach to missing data estimation using neural networks. Information Sciences, Vol.237, pp 49-58
Abstract: This paper develops and presents a novel technique for missing data estimation using a combination of dynamic programming, neural networks and genetic algorithms (GA) on suitable subsets of the input data. The method proposed here is well suited for decision making processes and uses the concept of optimality and the Bellman’s equation to estimate the missing data. The proposed approach is applied to an HIV/AIDS database and the results shows that the proposed method significantly outperforms a similar method where dynamic programming is not used. This paper also suggests a different way of formulating a missing data problem such that the dynamic programming is applicable to estimate the missing data.
Description: Copyright: 2013 Elsevier. This is the authors Post Print it is posted here by permission granted by Elsevier. The definitive version will be published in the Journal of Information Sciences
URI: 10.1016/j.ins.2009.10.008
ISSN: 0020-0255
Appears in Collections:Command control and information warfare
Infrastructure systems and operations
Information security
Logistics and quantitative methods
Digital intelligence
Mobile intelligent autonomous systems
General science, engineering & technology

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