  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 the readout window around the Mg II h&k lines
that encodes information on the dynamics of the solar chromosphere. Our MIL
models are not only able to predict whether flares occur within the next
$\sim$25 minutes with accuracies of around 90%, but are also able to explain
which spectral profiles were particularly important for their bag-level
prediction. This information can be used to highlight regions of interest in
ongoing IRIS observations in real-time and to identify candidates for typical
flare precursor spectral profiles. We use k-means clustering to extract groups
of spectral profiles that appear relevant for flare prediction. The recovered
groups show high intensity, triplet red wing emission and single-peaked h and k
lines, as found by previous works. They seem to be related to small-scale
explosive events that have been reported to occur tens of minutes before a
flare.
