Survey of Imbalanced Data Methodologies

• URL: http://arxiv.org/abs/2104.02240v1
• Date: Tue, 6 Apr 2021 02:10:22 GMT
• Title: Survey of Imbalanced Data Methodologies
• Authors: Lian Yu, Nengfeng Zhou
• Abstract summary: We 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.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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