Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Three new methods are used for estimating missing data in a
database using Neural Networks, Principal Component Analysis
and Genetic Algorithms are presented. The proposed methods
are tested on a set of data obtained from the South African
Antenatal Survey. The data is a collection of demographic
properties of patients. The proposed methods use Principal
Component Analysis to remove redundancies and reduce the
dimensionality in the data. Variations of autoassociative Neural
Networks are used to further reduce the dimensionality of the
data. A Genetic Algorithm is then used to find the missing data
by optimizing the error function of the three variants of the
Autoencoder Neural Network. The proposed system was tested
on data with 1 to 6 missing fields in a single record of data and
the accuracy of the estimated values were calculated and
recorded. All methods are as accurate as a conventional
feedforward neural network structure however the use of the
newly proposed methods employs neural network architectures
that have fewer hidden nodes.
