A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data
Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples...
| Main Authors: | , , |
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| Format: | Article |
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Elsevier BV
2019
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| Online Access: | http://hdl.handle.net/1721.1/120577 https://orcid.org/0000-0002-9507-5368 https://orcid.org/0000-0002-0289-5480 |