Survey of Imbalanced Data Methodologies

Lian Yu; Nengfeng Zhou

Survey of Imbalanced Data Methodologies

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arxiv 2104.02240 v1 pith:M4FBYJ6W submitted 2021-04-06 stat.ML cs.LG

Lian Yu , Nengfeng Zhou This is my paper · ORCID

classification stat.MLcs.LG

keywords datamethodologiesalgorithmsimbalancedmodelinganalyzedappliedclass-imbalance

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Imbalanced data set is a problem often found and well-studied in financial industry. In this paper, we reviewed and compared some popular methodologies handling data imbalance. We then applied the under-sampling/over-sampling methodologies to several modeling algorithms on UCI and Keel data sets. The performance was analyzed for class-imbalance methods, modeling algorithms and grid search criteria comparison.

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Absolute Evaluation Measures for Machine Learning: A Survey
cs.LG 2025-07 unverdicted novelty 1.0 of 10

A survey compiles bounded absolute evaluation metrics for classification, clustering, and ranking and proposes decision trees for metric selection, but several formulas are reproduced incorrectly.

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