Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
IEEE Transactions on Knowledge and Data Engineering
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
The Journal of Machine Learning Research
IEEE Transactions on Neural Networks
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
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This paper investigates the use of the Area Under the ROC Curve (AUC) as an alternative criteria for model selection in classification problems with unbalanced datasets. A novel algorithm, named here as AUCMLP, which incorporates AUC optimization into the Multi-layer Perceptron (MLPs) learning process is presented. The basic principle of AUCMLP is the solution of an optimization problem that aims at ranking quality as well as the separability of class distributions with respect to the threshold decision. Preliminary results achieved on real data, point out that our approach is promising, and can lead to better decision surfaces, specially under more severe unbalance conditions.