Multiclass Classification of Solar Flares in Imbalanced Data Using Ensemble Learning and Sampling Methods

Solar flares are intense bursts of radiation across the electromagnetic spectrum on the surface of the Sun. They are categorized into four classes: B, C, M, and X, depending on their intensity, with X-class flares being the strongest. Being able to predict a flare’s class before its occurrence is critical for anticipating the severity of its impact on Earth. We used the Space-weather HMI Active Region Patches (SHARP) parameters available from Stanford’s Joint Science Operations Center (JSOC) to train machine learning models to classify these flares. However, predicting the flare class is a challenging task, as it is a multiclass classification problem involving imbalanced data due to the small number of X-class flares in a solar cycle. We propose a new method that uses a combination of random undersampling and the synthetic minority oversampling technique (SMOTE) to combat the imbalanced data problem. Furthermore, we develop an ensemble algorithm that uses nine classifiers as base learners and logistic regression as meta-learner. Experimental results show that the proposed method is effective in predicting solar flares, especially the most intense X-class flares, within the next 24 hours.