Imbalanced classification is a well-known challenge faced by many real-world
applications. This issue occurs when the distribution of the target variable is
skewed, leading to a prediction bias toward the majority class. With the
arrival of the Big Data era, there is a pressing need for efficient solutions
to solve this problem. In this work, we present a novel resampling method
called SMOTENN that combines intelligent undersampling and oversampling using a
MapReduce framework. Both procedures are performed on the same pass over the
data, conferring efficiency to the technique. The SMOTENN method is
complemented with an efficient implementation of the neighborhoods related to
the minority samples. Our experimental results show the virtues of this
approach, outperforming alternative resampling techniques for small- and
medium-sized datasets while achieving positive results on large datasets with
reduced running times.

引入了一种名为 SMOTENN 的新型重采样方法，它通过使用 MapReduce 框架将智能欠采样和过采样结合在一起，从而解决了不平衡分类问题。实验结果表明，该方法在小型和中型数据集上优于替代重采样技术，并在大型数据集上达到了良好的效果且运行时间缩短。