Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BioMed Central Ltd
2021
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |