Using the real, gentle and modest AdaBoost learning algorithms to investigate the computerised associations between Coronal Mass Ejections and filaments

Space weather forecasting is a very challenging task and investigating the associations between properties (i.e., shape, scale, location) of the related solar features, appearing in solar images, are usually complicated because of the variation in their physical and visual properties. Establishing the correlations among the occurrences of solar activities and solar features is a long-standing problem in solar imaging. This work is an attempt to shed more light on the driving forces behind the initiations of Coronal Mass Ejections (CMEs). This is still a big mystery in this field and in this work we have analysed years of data relating to one particular feature, filaments, to determine if an association between filaments and the eruptions of CMEs can be drawn. The resulting association set has been fed to a powerful machine learning algorithm to determine if CMEs can be predicted solely based on filaments. Our learning algorithm, AdaBoost, is used because of robust and accurate performance. Three of the most common versions of the Adaboost algorithm are used in this work, which are the Gentle AdaBoost, the Real AdaBoost and the Modest AdaBoost.