Evaluating Competence Measures for Dynamic Regressor Selection

04/09/2019
by   Thiago J. M. Moura, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/01/2018

A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence

Dynamic classifier selection systems aim to select a group of classifier...
research
09/30/2018

META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning

Dynamic ensemble selection systems work by estimating the level of compe...
research
10/01/2018

FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection

Despite being very effective in several classification tasks, Dynamic En...
research
04/18/2018

K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection

Dynamic Ensemble Selection (DES) techniques aim to select locally compet...
research
11/03/2020

Classifier Pool Generation based on a Two-level Diversity Approach

This paper describes a classifier pool generation method guided by the d...
research
06/10/2018

EREL Selection using Morphological Relation

This work concentrates on Extremal Regions of Extremum Level (EREL) sele...
research
09/02/2021

Optimal subgroup selection

In clinical trials and other applications, we often see regions of the f...

Please sign up or login with your details

Forgot password? Click here to reset