Model generation of neural network ensembles using two-level cross-validation

This research investigates cross-validation techniques for performing neural network ensemble generation and performance evaluation. The chosen framework is the Neural Network Ensemble Simulator (NNES). Ensembles of classifiers are generated using level-one cross-validation. Extensive modeling is performed and evaluated using level-two cross-validation. NNES 4.0 automatically generates unique data sets for each student and each ensemble within a model. The results of this study confirm that level-one cross-validation improves ensemble model generation. Results also demonstrate the value of level-two cross-validation as a mechanism for measuring the true performance of a given model.