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

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 ma...

Full description

Saved in:
Bibliographic Details
Published inArtificial Intelligence Techniques for Rational Decision Making pp. 55 - 71
Main Author Marwala, Tshilidzi
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesAdvanced Information and Knowledge Processing
Subjects
Conjugate Gradient Method
Genetic Algorithm
Hide Unit
Hyperbolic Tangent Function
Network Weight
Online AccessGet full text
ISBN3319114239
9783319114231
ISSN1610-3947
DOI10.1007/978-3-319-11424-8_4

Cover

More Information
Summary: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.
ISBN:3319114239
9783319114231
ISSN:1610-3947
DOI:10.1007/978-3-319-11424-8_4