A Random Forests Quantile Classifier for Class Imbalanced Data.

Extending previous work on quantile classifiers (-classifiers) we propose the *-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 * 1, where * equals the unconditional probability of observing a minority class sample. The motivation for *-classification stems from a density-based approach and leads to the useful property that the *-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the *-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply *-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to -mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.