Using Multiple Instance Learning for Explainable Solar Flare Prediction. (arXiv:2203.13896v2 [astro-ph.SR] UPDATED)

In this work we leverage a weakly-labeled dataset of spectral data from NASAs
IRIS satellite for the prediction of solar flares using the Multiple Instance
Learning (MIL) paradigm. While standard supervised learning models expect a
label for every instance, MIL relaxes this and only considers bags of instances
to be labeled. This is ideally suited for flare prediction with IRIS data that
consists of time series of bags of UV spectra measured along the instrument
slit. In particular, we consider …

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