Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
With balanced training sample (TS) data, learning algorithms offer good results in lithology classification. Meanwhile, unprecedented lithological mapping in remote places is predicted to be difficult, resulting in limited and unbalanced samples. To address this issue, we can use a variety of techni...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-08-01
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| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2022-0487 |