Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance
This addresses class imbalance for practitioners in fields like fraud detection, but it is incremental as it builds on existing ensemble and resampling techniques.
The paper tackles class imbalance problems in domains like financial fraud detection by introducing DeepBalance, an ensemble of deep belief networks with balanced bootstraps and random feature selection, and demonstrates that it outperforms baseline methods such as SMOTE in AUC and sensitivity on a highly imbalanced financial transaction dataset.
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 a highly imbalanced financial transaction data. 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.