TY - GEN
T1 - Treatment of missing data using neural networks and genetic algorithms
AU - Abdella, Mussa
AU - Marwala, Tshilidzi
PY - 2005
Y1 - 2005
N2 - This paper introduces a 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. An investigation on using the proposed method to accurately approximate missing data as the number of missing cases within a single record increases is conducted. Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are employed. Results obtained using RBF are found to be better than those from the MLP. Results from a combination of both MLP and RBF are found to be better than those obtained using either MLP or RBF individually.
AB - This paper introduces a 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. An investigation on using the proposed method to accurately approximate missing data as the number of missing cases within a single record increases is conducted. Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are employed. Results obtained using RBF are found to be better than those from the MLP. Results from a combination of both MLP and RBF are found to be better than those obtained using either MLP or RBF individually.
UR - http://www.scopus.com/inward/record.url?scp=33745967950&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2005.1555899
DO - 10.1109/IJCNN.2005.1555899
M3 - Conference contribution
AN - SCOPUS:33745967950
SN - 0780390482
SN - 9780780390485
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 598
EP - 603
BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
T2 - International Joint Conference on Neural Networks, IJCNN 2005
Y2 - 31 July 2005 through 4 August 2005
ER -