Missing Data Approaches for Rational Decision Making: Application to Antenatal Data

• Published in: Artificial Intelligence Techniques for Rational Decision Making pp. 55 - 71
• Main Author: Marwala, Tshilidzi
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2014
• Series: Advanced Information and Knowledge Processing
• Subjects: Conjugate Gradient Method; Genetic Algorithm; Hide Unit; Hyperbolic Tangent Function; Network Weight
• ISBN: 3319114239; 9783319114231
• ISSN: 1610-3947
• DOI: 10.1007/978-3-319-11424-8_4

This chapter introduces missing data estimation for rational decision making. In this chapter it is assumed that there is a fixed topological characteristic between the variables required to make a rational decision and the actual rational decision. This, therefore, implies that rational decision making can be viewed as a missing data in a topology that includes both the action variables and the decision. This technique is applied using an autoassociative multi-layer perceptron network trained using scaled conjugate method and the missing data is estimated using genetic algorithm. This technique is used to predict HIV status of a subject given the demographic characteristics.