Efficient Hybrid Oversampling and Intelligent Undersampling for
  Imbalanced Big Data Classification
Carla Vairetti José Luis Assadi Sebastián Maldonado
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.