Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results
Roweida Mohammed, Jumanah Rawashdeh, Malak Abdullah
Abstract
Data imbalance in Machine Learning refers to an unequal distribution of classes within a dataset. This issue is encountered mostly in classification tasks in which the distribution of classes or labels in a given dataset is not uniform. The straightforward method to solve this problem is the resampling method by adding records to the minority class or deleting ones from the majority class. In this paper, we have experimented with the two resampling widely adopted techniques: oversampling and undersampling. In order to explore both techniques, we have chosen a public imbalanced dataset from kaggle website Santander Customer Transaction Prediction and have applied a group of well-known machine learning algorithms with different hyperparamters that give best results for both resampling techniques. One of the key findings of this paper is noticing that oversampling performs better than undersampling for different classifiers and obtains higher scores in different evaluation metrics.