TY - GEN
T1 - The use of genetic algorithms and neural networks to approximate missing data in database
AU - Abdella, Mussa
AU - Marwala, Tshilidzi
PY - 2005
Y1 - 2005
N2 - Missing data creates various problems in analysing and processing data in databases. In this paper we introduce 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. Multi-Layer Perception (MLP) and Radial Basis Function (RBF) networks are employed to train the neural networks. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior to MLP.
AB - Missing data creates various problems in analysing and processing data in databases. In this paper we introduce 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. Multi-Layer Perception (MLP) and Radial Basis Function (RBF) networks are employed to train the neural networks. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior to MLP.
UR - http://www.scopus.com/inward/record.url?scp=33749072448&partnerID=8YFLogxK
U2 - 10.1109/ICCCYB.2005.1511574
DO - 10.1109/ICCCYB.2005.1511574
M3 - Conference contribution
AN - SCOPUS:33749072448
SN - 0780391225
SN - 9780780391222
T3 - ICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings
SP - 207
EP - 212
BT - ICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings
T2 - ICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics
Y2 - 13 April 2005 through 16 April 2005
ER -