  Binary classification with an imbalanced dataset is challenging. Models tend
to consider all samples as belonging to the majority class. Although existing
solutions such as sampling methods, cost-sensitive methods, and ensemble
learning methods improve the poor accuracy of the minority class, these methods
are limited by overfitting problems or cost parameters that are difficult to
decide. We propose HADR, a hybrid approach with dimension reduction that
consists of data block construction, dimentionality reduction, and ensemble
learning with deep neural network classifiers. We evaluate the performance on
eight imbalanced public datasets in terms of recall, G-mean, and AUC. The
results show that our model outperforms state-of-the-art methods.
