Importance of feature selection stability in the classifier evaluation on high-dimensional genetic data
Classifiers trained on high-dimensional data, such as genetic datasets, often encounter situations where the number of features exceeds the number of objects. In these cases, classifiers typically rely on a small subset of features. For a robust algorithm, this subset should remain relatively stable...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-11-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/18405.pdf |