A SVM integrated Case Based Learning Data GA for Solar Flare Prediction.

Unusual intense solar flare can have serious impact on the human society. In particular, it may cause serious problems such as damaging electric power plants. It is desirable but difficult to predict intense solar flare because of imbalanced classification problems. To overcome this, we developed Case Based Genetic Algorithm (GA) integrated with Local Optimizer (CBGALO). Here, a Support Vector Machine (SVM) is used as Local Optimizer. However, the prediction precision for learning significantly depends on input data. Therefore, CBGALO was elaborated to extend by a Case Based GA that is able to automatically restart. This forms a good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the currently popular deep learning cannot search the input data for learning automatically or at least evolutionarily. The effect of our approach is proven in predicting X class solar flare as follows: 1) extended CBGALO reached more than 85% of precision in most (12 out of 14) cases and 91.2% at maximum, 2) previous CBGALO reached 84% at most 3) other approaches in the same environment reached less than 75%.