Survey on Imbalanced Data, Representation Learning and SEP Forecasting
Josias Moukpe
Deep Learning methods have significantly advanced various data-driven tasks
such as regression, classification, and forecasting. However, much of this
progress has been predicated on the strong but often unrealistic assumption
that training datasets are balanced with respect to the targets they contain.
This misalignment with real-world conditions, where data is frequently
imbalanced, hampers the effectiveness of such models in practical applications.
Methods that reconsider that assumption and tackle real-world imbalances have
begun to emerge and explore avenues to address this challenge. One such
promising avenue is representation learning, which enables models to capture
complex data characteristics and generalize better to minority classes. By
focusing on a richer representation of the feature space, these techniques hold
the potential to mitigate the impact of data imbalance. In this survey, we
present deep learning works that step away from the balanced-data assumption,
employing strategies like representation learning to better approximate
real-world imbalances. We also highlight a critical application in SEP
forecasting where addressing data imbalance is paramount for success.