  In this paper, we consider incorporating data associated with the sun's north
and south polar field strengths to improve solar flare prediction performance
using machine learning models. When used to supplement local data from active
regions on the photospheric magnetic field of the sun, the polar field data
provides global information to the predictor. While such global features have
been previously proposed for predicting the next solar cycle's intensity, in
this paper we propose using them to help classify individual solar flares. We
conduct experiments using HMI data employing four different machine learning
algorithms that can exploit polar field information. Additionally, we propose a
novel probabilistic mixture of experts model that can simply and effectively
incorporate polar field data and provide on-par prediction performance with
state-of-the-art solar flare prediction algorithms such as the Recurrent Neural
Network (RNN). Our experimental results indicate the usefulness of the polar
field data for solar flare prediction, which can improve Heidke Skill Score
(HSS2) by as much as 10.1%.
