Box drawings for learning with imbalanced data
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers are disjun...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2015
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| Online Access: | http://hdl.handle.net/1721.1/99143 https://orcid.org/0000-0001-7563-0961 |