Balanced Split: A new train-test data splitting strategy for imbalanced datasets
This work addresses the challenge of imbalanced datasets for machine learning practitioners, though it appears incremental as it builds on existing data-level approaches.
The paper tackles the problem of class imbalance in classification datasets by introducing a new data-splitting strategy called balanced split, which addresses disadvantages of common splitting methods to improve classification accuracy.
Classification data sets with skewed class proportions are called imbalanced. Class imbalance is a problem since most machine learning classification algorithms are built with an assumption of equal representation of all classes in the training dataset. Therefore to counter the class imbalance problem, many algorithm-level and data-level approaches have been developed. These mainly include ensemble learning and data augmentation techniques. This paper shows a new way to counter the class imbalance problem through a new data-splitting strategy called balanced split. Data splitting can play an important role in correctly classifying imbalanced datasets. We show that the commonly used data-splitting strategies have some disadvantages, and our proposed balanced split has solved those problems.