# Evaluating Competence Measures for Dynamic Regressor Selection

## TL;DR

This paper evaluates and adapts various competence measures for dynamic regressor selection in ensemble systems, demonstrating their effectiveness across multiple datasets and highlighting the importance of measure choice depending on the problem.

## Contribution

It reviews and adapts eight competence measures for DRS in regression, providing a comprehensive experimental comparison across 15 datasets.

## Key findings

- DRS systems outperform individual regressors and static methods
- The effectiveness of competence measures varies by problem
- Choice of competence measure is problem-dependent

## Abstract

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.