  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.
