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Class imbalance problems manifest in domains such as financial frauddetection or network intrusion analysis, where the prevalence of one class ismuch higher than another. Typically, practitioners are more interested inpredicting the minority class than the majority class as the minority class maycarry a higher misclassification cost. However, classifier performancedeteriorates in the face of class imbalance as oftentimes classifiers maypredict every point as the majority class. Methods for dealing with classimbalance include cost-sensitive learning or resampling techniques. In thispaper, we introduce DeepBalance, an ensemble of deep belief networks trainedwith balanced bootstraps and random feature selection. We demonstrate that ourproposed method outperforms baseline resampling methods such as SMOTE andunder- and over-sampling in metrics such as AUC and sensitivity when applied toa highly imbalanced financial transaction data. Additionally, we exploreperformance and training time implications of various model parameters.Furthermore, we show that our model is easily parallelizable, which can reducetraining times. Finally, we present an implementation of DeepBalance in R.

Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance