Classification of imbalanced cloud image data using deep neural networks: performance improvement through a data science competition
In one sentence. Competition-derived deep networks with oversampling, ensembles and cost-sensitive losses classify imbalanced cloud images for tropical cyclone precursors, improving performance by 65.4% over conventional methods.
Abstract
Abstract Image data classification using machine learning is an effective method for detecting atmospheric phenomena. However, extreme weather events with a small number of cases cause a decrease in classification prediction accuracy owing to the imbalance in data between the target class and the other classes. To build a highly accurate classification model, I held a data analysis competition to determine the best classification performance for two classes of cloud image data, specifically tropical cyclones including precursors and other classes. For the top models in the competition, minority data oversampling, majority data undersampling, ensemble learning, deep layer neural networks, and cost-effective loss functions were used to improve the classification performance of the imbalanced data. In particular, the best model of 209 submissions succeeded in improving the classification capability by 65.4% over similar conventional methods in a measure of the low false alarm ratio.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.1186/s40645-021-00459-y OpenAlex:W4225697294
- Added
- 2026-10-22
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