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A dynamic programming approach to missing data estimation using neural networks

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dc.contributor.author Nelwamondo, Fulufhelo V
dc.contributor.author Golding, D
dc.contributor.author Marwala, T
dc.date.accessioned 2010-07-28T09:01:03Z
dc.date.available 2010-07-28T09:01:03Z
dc.date.issued 2013
dc.identifier.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 en
dc.identifier.issn 0020-0255
dc.identifier.uri 10.1016/j.ins.2009.10.008
dc.identifier.uri http://hdl.handle.net/10204/4134
dc.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 en
dc.description.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. en
dc.language.iso en en
dc.publisher Elsevier en
dc.subject Missing data techniques en
dc.subject Genetic algorithms en
dc.subject Bellman's Equation en
dc.subject Dynamic programming en
dc.subject Neural networks en
dc.subject Data imputation en
dc.title A dynamic programming approach to missing data estimation using neural networks en
dc.type Article en
dc.identifier.apacitation Nelwamondo, F. V., Golding, D., & Marwala, T. (2013). A dynamic programming approach to missing data estimation using neural networks. http://hdl.handle.net/10204/4134 en_ZA
dc.identifier.chicagocitation Nelwamondo, Fulufhelo V, D Golding, and T Marwala "A dynamic programming approach to missing data estimation using neural networks." (2013) http://hdl.handle.net/10204/4134 en_ZA
dc.identifier.vancouvercitation Nelwamondo FV, Golding D, Marwala T. A dynamic programming approach to missing data estimation using neural networks. 2013; http://hdl.handle.net/10204/4134. en_ZA
dc.identifier.ris TY - Article AU - Nelwamondo, Fulufhelo V AU - Golding, D AU - Marwala, T AB - 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. DA - 2013 DB - ResearchSpace DP - CSIR KW - Missing data techniques KW - Genetic algorithms KW - Bellman's Equation KW - Dynamic programming KW - Neural networks KW - Data imputation LK - https://researchspace.csir.co.za PY - 2013 SM - 0020-0255 T1 - A dynamic programming approach to missing data estimation using neural networks TI - A dynamic programming approach to missing data estimation using neural networks UR - http://hdl.handle.net/10204/4134 ER - en_ZA


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