In: Computing and Informatics
The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database
M. ABDELLA - T. MARWALA
Vydavateľ / Publisher:
Rok, strany / Year, pages: 2005, 577-589
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Publikované / Published: 0000-00-00
URL na originálny zdroj / Original source URL:
Typ dokumentu / Type of Document:
Popis / Abstract:
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 Perceptron (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 toMLP.
Ako citovať / How to Cite:
ABDELLA, M., MARWALA, T. 2005. The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database. In Computing and Informatics, vol. 24, no.6, pp. 577-589.
ABDELLA, M., MARWALA, T. (2005). The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database. Computing and Informatics, 24(6), 577-589.
Kľúčové slová / Keywords:
Neural networks, genetic algorithms, multi-layer perceptron, radial basis function, missing data, error function, auto-associative
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