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

TL;DR

FIRE-DES++ improves dynamic ensemble selection by better identifying true indecision regions and reducing noise, leading to higher classification accuracy across multiple datasets.

Contribution

This paper introduces FIRE-DES++, an enhanced method that refines region of competence selection and noise handling for dynamic ensemble selection.

Findings

FIRE-DES++ outperforms FIRE-DES in 7 of 8 DES techniques.

FIRE-DES++ achieves superior accuracy on 64 datasets.

Enhanced region definition improves classifier selection.

Abstract

Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correctly classify at least one pair of samples from different classes in the region of competence of the test sample. However, FIRE-DES applies the pre-selection for the classification of a test sample if and only if its region of competence is composed of samples from different classes (indecision region), even though this criterion is not reliable for determining if a test sample is located close to the borders of classes (true indecision region) when the region of competence is obtained using classical nearest neighbors approach. Because of that, FIRE-DES mistakes noisy regions for true indecision regions, leading to the pre-selection of incompetent classifiers, and mistakes true indecision regions for safe regions, leaving samples in such regions without any pre-selection. To tackle these issues, we propose the FIRE-DES++, an enhanced FIRE-DES that removes noise and reduces the overlap of classes in the validation set; and defines the region of competence using an equal number of samples of each class, avoiding selecting a region of competence with samples of a single class. Experiments are conducted using FIRE-DES++ with 8 different dynamic selection techniques on 64 classification datasets. Experimental results show that FIRE-DES++ increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks.