We have investigated the performance of a generalisation error predictor, Gest, in the context of error correcting output coding ensembles based on multi-layer perceptrons. An experimental evaluation on benchmark datasets with added classification noise shows that over-fitting can be detected and a comparison is made with the Q measure of ensemble diversity. Each dichotomy associated with a column of an ECOC code matrix is presented with a bootstrap sample of the training set. Gest uses the out-of-bootstrap samples to efficiently estimate the mean column error for the independent test set and hence the test error. This estimate can then be used select a suitable complexity for the base classifiers in the ensemble. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Prior, M., & Windeatt, T. (2005). Over-fitting in ensembles of neural network classifiers within ECOC frameworks. In Lecture Notes in Computer Science (Vol. 3541, pp. 286–295). Springer Verlag. https://doi.org/10.1007/11494683_29
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