  Imbalanced domain learning aims to produce accurate models in predicting
instances that, though underrepresented, are of utmost importance for the
domain. Research in this field has been mainly focused on classification tasks.
Comparatively, the number of studies carried out in the context of regression
tasks is negligible. One of the main reasons for this is the lack of loss
functions capable of focusing on minimizing the errors of extreme (rare)
values. Recently, an evaluation metric was introduced: Squared Error Relevance
Area (SERA). This metric posits a bigger emphasis on the errors committed at
extreme values while also accounting for the performance in the overall target
variable domain, thus preventing severe bias. However, its effectiveness as an
optimization metric is unknown. In this paper, our goal is to study the impacts
of using SERA as an optimization criterion in imbalanced regression tasks.
Using gradient boosting algorithms as proof of concept, we perform an
experimental study with 36 data sets of different domains and sizes. Results
show that models that used SERA as an objective function are practically better
than the models produced by their respective standard boosting algorithms at
the prediction of extreme values. This confirms that SERA can be embedded as a
loss function into optimization-based learning algorithms for imbalanced
regression scenarios.
