Abstract
Backpropagation neural networks have been applied to prediction and classification problems in many real world situations. However, a drawback of this type of neural network is that it requires a full set of input data, and real world data is seldom complete. We have investigated two ways of dealing with incomplete data — network reduction using multiple neural network classifiers, and value substitution using estimated values from predictor networks — and compared their performance with an induction method. On a thyroid disease database collected in a clinical situation, we found that the network reduction method was superior. We conclude that network reduction can be a useful method for dealing with missing values in diagnostic systems based on backpropagation neural networks.
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Sharpe, P.K., Solly, R.J. Dealing with missing values in neural network-based diagnostic systems. Neural Comput & Applic 3, 73–77 (1995). https://doi.org/10.1007/BF01421959
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DOI: https://doi.org/10.1007/BF01421959