  Class imbalance poses a major challenge in different classification tasks,
which is a frequently occurring scenario in many real-world applications. Data
resampling is considered to be the standard approach to address this issue. The
goal of the technique is to balance the class distribution by generating new
samples or eliminating samples from the data. A wide variety of sampling
techniques have been proposed over the years to tackle this challenging
problem. Sampling techniques can also be incorporated into the ensemble
learning framework to obtain more generalized prediction performance. Balanced
Random Forest (BRF) and SMOTE-Bagging are some of the popular ensemble
approaches. In this study, we propose a modification to the BRF classifier to
enhance the prediction performance. In the original algorithm, the Random
Undersampling (RUS) technique was utilized to balance the bootstrap samples.
However, randomly eliminating too many samples from the data leads to
significant data loss, resulting in a major decline in performance. We propose
to alleviate the scenario by incorporating a novel hybrid sampling approach to
balance the uneven class distribution in each bootstrap sub-sample. Our
proposed hybrid sampling technique, when incorporated into the framework of the
Random Forest classifier, termed as iBRF: improved Balanced Random Forest
classifier, achieves better prediction performance than other sampling
techniques used in imbalanced classification tasks. Experiments were carried
out on 44 imbalanced datasets on which the original BRF classifier produced an
average MCC score of 47.03% and an F1 score of 49.09%. Our proposed algorithm
outperformed the approach by producing a far better MCC score of 53.04% and an
F1 score of 55%. The results obtained signify the superiority of the iBRF
algorithm and its potential to be an effective sampling technique in imbalanced
learning.
