Introducing DeepBalance: Random deep belief network ensembles to address class imbalance

Class imbalance problems manifest in domains such as financial fraud detection or network intrusion analysis, where the prevalence of one class is much higher than another. Typically, practitioners are more interested in predicting the minority class than the majority class, as the minority class may carry a higher misclassification cost. However, classifier performance deteriorates in the face of class imbalance as oftentimes classifiers may predict every point as the majority class. Methods for dealing with class imbalance include cost-sensitive learning or resampling techniques. In this paper, we introduce DeepBalance, an ensemble of deep belief networks trained with balanced bootstraps and random feature selection. We demonstrate that our proposed method outperforms baseline resampling methods such as SMOTE and under- and over-sampling in metrics such as AUC and sensitivity when applied to highly imbalanced financial transaction data sets. Additionally, we explore performance and training time implications of various model parameters. Furthermore, we show that our model is easily parallelizable, which can reduce training times. Finally, we present an implementation of DeepBalance in R.