Class imbalance is a frequently occurring scenario in classification tasks.
Learning from imbalanced data poses a major challenge, which has instigated a
lot of research in this area. Data preprocessing using sampling techniques is a
standard approach to deal with the imbalance present in the data. Since
standard classification algorithms do not perform well on imbalanced data, the
dataset needs to be adequately balanced before training. This can be
accomplished by oversampling the minority class or undersampling the majority
class. In this study, a novel hybrid sampling algorithm has been proposed. To
overcome the limitations of the sampling techniques while ensuring the quality
of the retained sampled dataset, a sophisticated framework has been developed
to properly combine three different sampling techniques. Neighborhood Cleaning
rule is first applied to reduce the imbalance. Random undersampling is then
strategically coupled with the SMOTE algorithm to obtain an optimal balance in
the dataset. This proposed hybrid methodology, termed "SMOTE-RUS-NC", has been
compared with other state-of-the-art sampling techniques. The strategy is
further incorporated into the ensemble learning framework to obtain a more
robust classification algorithm, termed "SRN-BRF". Rigorous experimentation has
been conducted on 26 imbalanced datasets with varying degrees of imbalance. In
virtually all datasets, the proposed two algorithms outperformed existing
sampling strategies, in many cases by a substantial margin. Especially in
highly imbalanced datasets where popular sampling techniques failed utterly,
they achieved unparalleled performance. The superior results obtained
demonstrate the efficacy of the proposed models and their potential to be
powerful sampling algorithms in imbalanced domain.

研究提出了一种新型的样本采样算法，“SMOTE-RUS-NC”，它可以通过结合三种不同的采样技术来实现数据的平衡处理，进而提高分类算法的性能，特别是能够在高度不平衡的数据集中表现出卓越的效果。