A dynamic programming approach to missing data estimation using neural networks

Authors:
Fulufhelo V. Nelwamondo;Dan Golding;Tshilidzi Marwala
Affiliations:
Modelling and Digital Science Unit, Council for Scientific and Industrial Research, P.O. Box 91230, Auckland Park, 2006, Johannesburg, South Africa and Faculty of Engineering and the Built Environ ...;Modelling and Digital Science Unit, Council for Scientific and Industrial Research, P.O. Box 91230, Auckland Park, 2006, Johannesburg, South Africa;Faculty of Engineering and the Built Environment, University of Johannesburg, P.O. Box 524, Auckland Park, 2006, Johannesburg, South Africa
Venue:
Information Sciences: an International Journal
Year:
2013

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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.