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.
Reference:
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
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
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
Nelwamondo FV, Golding D, Marwala T. A dynamic programming approach to missing data estimation using neural networks. 2013; http://hdl.handle.net/10204/4134.
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