Abstract:
Missing data creates various problems in analysing and processing of
data in databases. Due to this reason missing data has been an area of
research in various disciplines for a quite long time. This report intro-
duces a new method aimed at approximating missing data in a database
using a combination of genetic algorithms and neural networks. The
proposed method uses genetic algorithm to minimise an error function
derived from an auto-associative neural network. The error function is
expressed as the square of the di®erence between the actual observa-
tions and predicted values from an auto-associative neural network. In
the event of missing data, all the values of the actual observations are
not known hence, the error function is decomposed to depend on the
known and unknown (missing) values. Multi Layer Perceptron (MLP),
and Radial Basis Function (RBF) neural networks are employed to train
the neural networks. The research focus also lies on the investigation
of using the proposed method in approximating missing data with great
accuracy as the number of missing cases within a single record increases.
The research also investigates the impact of using di®erent neural net-
work architecture in training the neural network and the approximation
ii
found to the missing values. It is observed that approximations of miss-
ing data obtained using the proposed model to be highly accurate with
95% correlation coe±cient between the actual missing values and cor-
responding approximated values using the proposed model. It is found
that results obtained using RBF are better than MLP. Results found us-
ing the combination of both MLP and RBF are found to be better than
those obtained using either MLP or RBF. It is also observed that there
is no signi¯cant reduction in accuracy of results as the number of missing
cases in a single record increases. Approximations found for missing data
are also found to depend on the particular neural network architecture
employed in training the data set.
