TITLE: Balanced Split: A new train-test data splitting strategy for imbalanced datasets 

ABSTRACT: 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.
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.
Balanced Split: A new train-test data splitting strategy for imbalanced datasets
http://arxiv.org/abs/2212.11116v1