On the Selection of the Competence Measure for Dynamic Regressor Selection

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to predict the target value of a query pattern. This competence is calculated using the performance of the regressors in a local region of the feature space around the query pattern that is called the region of competence. Nonetheless, defining the correct measure to compute the degree of competence of the regressors is a hard task. In this work, we propose a new technique to DRS that selects the best competence measure for a given dataset. To validate our technique, we perform a set of comprehensive experiments on 15 regression datasets. The proposed technique can operate in three different fashions: (i) selection of the most competent regressor; (ii) combination of all regressors from the ensemble; and (iii) selection of a subset composed of the most competent ones and combine them. The proposals are compared against DRS algorithms, individual regressors, and static systems that use the Mean and the Median as a fusion strategy. The results show that the proposed technique, which chooses a different competence measure per task, outperforms literature techniques.