CSC '92 Proceedings of the 1992 ACM annual conference on Communications
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Classification of seismic signals by integrating ensembles ofneural networks
IEEE Transactions on Signal Processing
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The literature suggests that an ensemble of classifiers outperforms a single classifier across a range of classification problems. This paper investigates the application of an ensemble of neural network classifiers to the prediction of potential defaults for a set of personal loan accounts drawn from a medium sized Australian financial institution. The imbalanced nature of the data sets necessitates the implementation of strategies to avoid under learning of the minority class and two such approaches (minority over-sampling and majority under-sampling) were adopted here. The ensemble out performed the single networks irrespective of which strategy was used. The results also compared more than favourably with those reported in the literature for a similar application area.