Stability Problems with Artificial Neural Networks and the Ensemble Solution
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1999-10Citation:
Cunningham, Padraig; Carney, John G.; Jacob, Saji. 'Stability Problems with Artificial Neural Networks and the Ensemble Solution'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-1999-52, 1999, pp10Download Item:
Abstract:
Artificial Neural Networks (ANNs) are very popular as classification or regression
mechanisms in medical decision support systems despite the fact that they are unstable
predictors. This instability means that small changes in the training data used to build the
model (i.e. train the ANN) may result in very different models. A central implication of this is
that different sets of training data may produce models with very different generalisation
accuracies. In this paper we show in detail how this can happen in a prediction system for use
in In-Vitro Fertilisation. We argue that claims for the generalisation performance of ANNs
used in such a scenario should only be based on k-fold cross validation tests. We also show
how the accuracy of such a predictor can be improved by aggregating the output of several
predictors.
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Trinity College Dublin, Department of Computer ScienceType of material:
Technical ReportCollections
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Computer Science Technical ReportTCD-CS-1999-52
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