Neural Network Based Undersampling Techniques
This addresses the issue of model bias towards majority classes in real-life applications, but it is incremental as it builds on existing resampling methods.
The paper tackled the class imbalance problem in machine learning by proposing neural network-based undersampling algorithms, which outperformed most other resampling techniques in terms of AUC, F1, and G-mean scores.
Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most of which employed resampling, i.e. oversampling and undersampling techniques to bring the required balance in the data. In this paper, we propose neural network based algorithms for undersampling. Then we resampled several class imbalanced data using our algorithms and also some other popular resampling techniques. Afterwards we classified these undersampled data using some common classifier. We found out that our resampling approaches outperform most other resampling techniques in terms of both AUC, F1 and G-mean score.