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This is a Quantitative Study study.

Evaluation of the performance of the Smote, Smote Enn, and Borderline Smote resampling methods based on the number of outlier data with Z Score

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This analysis compares smote, smote enn, and borderline smote's effectiveness on outlier data, indicating their similar performance.

No significant difference was found in outlier data between smote, smote enn, and borderline smote.
In the diabetes dataset, outlier percentages before and after resampling were close, with a minor reduction.
Oversampling methods did not notably affect the overall classification performance metrics such as accuracy and precision.
Feature correlation was more impactful than achieving data balance in influencing classification outcomes.

Gunadi et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This analysis compares smote, smote enn, and borderline smote's effectiveness on outlier data, indicating their similar performance.

No significant difference was found in outlier data between smote, smote enn, and borderline smote.
In the diabetes dataset, outlier percentages before and after resampling were close, with a minor reduction.
Oversampling methods did not notably affect the overall classification performance metrics such as accuracy and precision.
Feature correlation was more impactful than achieving data balance in influencing classification outcomes.

Gunadi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark