Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification
This work addresses the problem of imbalanced classification for big data applications, offering an efficient solution that is incremental in nature.
The paper tackles imbalanced classification in big data by proposing SMOTENN, a novel resampling method combining intelligent undersampling and oversampling in a MapReduce framework, which outperforms alternatives on small- and medium-sized datasets and achieves positive results on large datasets with reduced running times.
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.