Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

Abstract

The research of flare forecast based on the machine learning algorithm is an important content of space science. In order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance, we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization (PSO)-based Support Vector Machine (SVM) regular term optimization method. Considering the problem of intra-class imbalance and inter-class imbalance in flare samples, we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique (SMOTE) oversampling method, and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority class. At the same time, for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise, In this research, on the basis of adding regularization parameters, the PSO algorithm is used to optimize the hyperparameters, which can maximize the performance of the classifier. Finally, through a comprehensive comparison test, it is proved that the method designed can be well applied to the flare forecast problem, and the effectiveness of the method is proved.