2008 International Conference on BioMedical Engineering and Informatics Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation May 27-May 30 ISBN: 978-0-7695-3118-2
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.251
The evaluation of an Artificial Neural Network is not a part of the training phase and it is not a trivial process. It represents an exhaustive test process with a computational effort greater than the ANN training. Monitoring the error during the training phase can provide an indicator of the convergence of the algorithm. This study presents some analysis tools integrated to the supervised training of the ANN MLP Classifier. The objective is to provide a quantitative evaluation of the learning and generalization of the knowledge during the ANN supervised training. The Cross Validation and the ROC Analysis procedures were used together with the standard back-propagation ANN MLP training algorithm. The procedure is described and the results of the ANN classifier for epilepsy events in EEG data are presented.
Index Terms:
ANN Classifier, ROC Analysis, Cross Validation, ANN Classifier Evaluation, Back-Propagation
Citation:
Miguel Antonio Sovierzoski, Fernanda Isabel Marques Argoud, Fernando Mendes de Azevedo, "Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation," bmei, vol. 1, pp.274-278, 2008 International Conference on BioMedical Engineering and Informatics, 2008 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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