  Dynamic regressor selection (DRS) systems work by selecting the most
competent regressors from an ensemble to estimate the target value of a given
test pattern. This competence is usually quantified using the performance of
the regressors in local regions of the feature space around the test pattern.
However, choosing the best measure to calculate the level of competence
correctly is not straightforward. The literature of dynamic classifier
selection presents a wide variety of competence measures, which cannot be used
or adapted for DRS. In this paper, we review eight measures used with
regression problems, and adapt them to test the performance of the DRS
algorithms found in the literature. Such measures are extracted from a local
region of the feature space around the test pattern, called region of
competence, therefore competence measures.To better compare the competence
measures, we perform a set of comprehensive experiments of 15 regression
datasets. Three DRS systems were compared against individual regressor and
static systems that use the Mean and the Median to combine the outputs of the
regressors from the ensemble. The DRS systems were assessed varying the
competence measures. Our results show that DRS systems outperform individual
regressors and static systems but the choice of the competence measure is
problem-dependent.
