Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results

By Roweida Mohammed, Jumanah Rawashdeh, Malak Abdullah. International Conference on Information, Communications and Signal Processing, 2020

601 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b5c14e750010e5351c13294fb10552bf3052359d

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Citations in the corpus

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “Random over-sampling (ROS) [236] and random under-sampling (RUS) [237, 238] are naive approaches to combat imbalanced dataset in order to balance the class distribution.”
Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization (2021, methodology): “Unfortunately a direct comparison with past research results cannot be made, however, putting it into perspective in [21] the same data set was investigated.”
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, background): “Overall, the hybrid resampling approach seems to work well in an extremely imbalanced condition, whereas ROS is not recommended due to its vulnerability to overﬁtting [48,49].”