Adaptive generation-based approaches of oversampling using different sets of base and nearest neighbor's instances
Standard classification algorithms often face a challenge of learning from imbalanced datasets. While several approaches have been employed in addressing this problem, methods that involve oversampling of minority samples remain more widely used in comparison to algorithmic modifications. Most varia...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science and Information Organization
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/100866/1/HatemSYNabus2022_AdaptiveGenerationbasedApproaches.pdf |