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Dynamic regressor selection (DRS) systems work by selecting the mostcompetent regressors from an ensemble to estimate the target value of a giventest pattern. This competence is usually quantified using the performance ofthe regressors in local regions of the feature space around the test pattern.However, choosing the best measure to calculate the level of competencecorrectly is not straightforward. The literature of dynamic classifierselection presents a wide variety of competence measures, which cannot be usedor adapted for DRS. In this paper, we review eight measures used withregression problems, and adapt them to test the performance of the DRSalgorithms found in the literature. Such measures are extracted from a localregion of the feature space around the test pattern, called region ofcompetence, therefore competence measures.To better compare the competencemeasures, we perform a set of comprehensive experiments of 15 regressiondatasets. Three DRS systems were compared against individual regressor andstatic systems that use the Mean and the Median to combine the outputs of theregressors from the ensemble. The DRS systems were assessed varying thecompetence measures. Our results show that DRS systems outperform individualregressors and static systems but the choice of the competence measure isproblem-dependent.

Evaluating Competence Measures for Dynamic Regressor Selection