Abstract
Image data classification using machine learning is one of the effective methods 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 of data between the target class and the other classes. In order to build a highly accurate classification model, we held a data analysis competition to determine the best classification performance for two classes of cloud image data: 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 imbalanced classification performance. In particular, the best model out of 209 submissions succeeded in improving the classification capability by 65.4% over similar conventional methods in a measure of low false alarm ratio.