Nelwamondo, Fulufhelo VGolding, DMarwala, T2010-07-282010-07-282013Nelwamondo, FV, Golding, D and Marwala, T. 2013. Dynamic programming approach to missing data estimation using neural networks. Information Sciences, Vol.237, pp 49-580020-025510.1016/j.ins.2009.10.008http://hdl.handle.net/10204/4134Copyright: 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 SciencesThis 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.enMissing data techniquesGenetic algorithmsBellman's EquationDynamic programmingNeural networksData imputationA dynamic programming approach to missing data estimation using neural networksArticleNelwamondo, F. V., Golding, D., & Marwala, T. (2013). A dynamic programming approach to missing data estimation using neural networks. http://hdl.handle.net/10204/4134Nelwamondo, Fulufhelo V, D Golding, and T Marwala "A dynamic programming approach to missing data estimation using neural networks." (2013) http://hdl.handle.net/10204/4134Nelwamondo FV, Golding D, Marwala T. A dynamic programming approach to missing data estimation using neural networks. 2013; http://hdl.handle.net/10204/4134.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 -