Enhancing model robustness to imbalanced species abundance distributions: Eliminating misclassified records via a model-agnostic approach, exemplified by tuna fisheries datasets
Anomalies in species abundance data can potentially cause classification errors in ecological forecasting models. Accurate estimation of anomalies locations can enhance the predictive capacity of models. This study aims to propose an approach for precisely identifying and correcting anomalies within...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004473 |
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