Ensemble of Feedforward Neural Networks Applied to Credit Default

Estrada, Juan Luis Herrero; Buquicchio, Luke J; Harding, Everett J

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Ensemble of Feedforward Neural Networks Applied to Credit Default

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This project combines feedforward neural networks (FFNN) with ensemble learning in a classification model applied to credit card default. To solve this problem, we generate FFNNs as the members of the ensemble. Each FFNN in the ensemble is built using different subsets of predictors as inputs and a separate predictor as the target (instead of the label). The output from the final hidden layer of each network is aggregated into a new dataset. A classification model then uses this new dataset to predict credit defaults. The classification model is evaluated using various metrics to show that an increase in ensemble size increases the consistency of the performance of the model.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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