Imbalanced datasets, where the number of instances in one class significantly outnumbers the other class(es), can hinder the performance of machine learning algorithms. While SMOTE (Synthetic Minority Over-sampling TEchnique) is a popular oversampling method, there are several alternatives that may be more effective depending on the specific characteristics of the data and learning task.
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ADASYN (Adaptive Synthetic Sampling) generates more synthetic examples for minority class samples that are harder to learn, as determined by the k-nearest neighbors[1][2].
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Borderline-SMOTE generates synthetic examples only for minority instances near the decision boundary between classes[2].
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SMOTE-ENN and SMOTE-Tomek combine SMOTE oversampling with undersampling techniques like Edited Nearest Neighbors (ENN) or Tomek links to remove noisy and borderline examples[2][3].
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Random Oversampling and Undersampling involve randomly duplicating minority class examples or removing majority class examples[3].
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SMOTE-CUT (Clustered Undersampling Technique) combines oversampling, clustering, and undersampling. It implements SMOTE, clusters both the original and result, and removes majority class samples from clusters[2].
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SMOTE-NC (Nominal Continuous) is a variant of SMOTE that can handle categorical data[2].
Cost-sensitive boosting algorithms like AdaCost and CSB2 assign higher misclassification costs to the minority class, influencing sample weights during boosting iterations. However, they don’t explicitly involve resampling.
While SMOTE is widely used, experiments have shown that it does not consistently improve performance on imbalanced datasets. The optimal strategy depends on the specific data and learning task. It is recommended to try multiple methods and compare their performance to find the best approach for a given problem.
Citations: [1] https://stats.stackexchange.com/questions/397204/what-are-other-ways-of-doing-oversampling-apart-from-smote [2] https://www.kdnuggets.com/2023/01/7-smote-variations-oversampling.html [3] https://towardsdatascience.com/stop-using-smote-to-handle-all-your-imbalanced-data-34403399d3be