Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data
Positive unlabeled (PU) learning is an important problem motivated by the occurrence of this type of partial observability in many applications. The present paper reconsiders recent advances in parametric modeling of PU data based on empirical likelihood maximization and argues that they can be sign...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-06-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.34768/amcs-2022-0022 |