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

Abstract

Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult the classifier selection. This fact makes the performance of most dynamic selection systems no better than static selections. In this paper, we demonstrate that the performance dynamic selection systems end up limited by the quality of the regions extracted. Thereafter, we propose a new dynamic classifier selection that improves the regions of competence in order to achieve higher recognition rates. obtained from several classification databases show the proposed method not only increase the recognition performance but also decreases the computational cost.

Paper published on IJCNN 2011

Cited by in corpus (5)

• META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
• META-DES.Oracle: Meta-learning and feature selection for ensemble selection
• Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
• On Meta-Learning for Dynamic Ensemble Selection
• Analyzing different prototype selection techniques for dynamic classifier and ensemble selection