Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme
Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. However, most real-world datasets are noisy, imbalanced, and contain redundant samples, prompting the need to address these problems before the datase...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10982066/ |
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