Imbalanced learning is a challenging task in predictive modeling and machine learning that has inspired many researchers to attempt to improve the existing algorithms for more accurate predictions of imbalanced data sets. Due to the nature of rare events, developing reliable and efficient classification models for imbalanced data has not been easily accomplished, and over the past two decades, various methods have been proposed. To this end, we propose a Linear Programming Support Vector Machine (LP-SVM) model to address the issue of imbalanced learning in weather applications. To further improve the model's predictive accuracy, we have implemented a parameter selection method based on the multi-objective parametric simplex approach for parameter tuning of LP-SVM. For numerical tests, we have used a real data set consisting of weather observations made by the Bureau of Meteorology's (BM) system in Australia. The results obtained from training and testing the model demonstrate the effectiveness of our proposed model on the tested examples.

Detecting rare events is a challenging task among machine learning practitioners, motivating them to navigate and further improve data processing and algorithmic approaches to find accurate and computationally efficient methods for imbalanced learning. Imbalanced data is common in weather prediction, where the massive size of data poses storage and computational challenges. To learn from imbalanced data, the algorithms must strive to learn each class precisely to be able to classify both the minority and the majority class. While creating complex models is somewhat necessary for such problems, the machine learning algorithm must have adequate generalization capabilities. In recent years, federated learning has been used as an environmentally-friendly approach, which can produce accurate results in a distributed setting. Although privacy is not a concern of this study, inherent characteristics of federated learning are beneficial in weather prediction. Deep learning combined with data augmentation is explored in the framework of federated learning. We compare multiple data augmentation methods in a centralized and federated learning framework. Federated learning has great potential for weather prediction tasks. By leveraging data from multiple sources, we were able to improve the accuracy and generalization of the classifier. Addressing the issue of imbalanced data is an essential step. Incorporating the two remarkable approaches of Generative Adversarial Networks and Synthetic Minority Over-sampling Technique is a suitable solution for tabular weather data, and our experimental results confirm the effectiveness of this approach.