Online local pool generation for dynamic classifier selection: an extended version

TL;DR

This paper introduces an online local pool generation method that improves dynamic classifier selection by focusing on difficult regions of the feature space, leading to higher recognition rates and better performance than existing global pool schemes.

Contribution

The work proposes a novel online local pool generation approach that adapts to local data difficulty, enhancing classifier selection accuracy in challenging regions.

Findings

Significantly higher recognition rates compared to global pool methods.

Performance surpasses three state-of-the-art classification models.

Statistically equivalent results to five advanced classifiers.

Abstract

Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifier's competence, the manner in which the pool is generated could affect the choice of the best classifier for a given sample. That is, the global perspective in which pools are generated may not help the DCS techniques in selecting a competent classifier for samples that are likely to be mislabelled. Thus, we propose in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space. The difficulty of a given area is determined by the classification difficulty of the samples in it. That way, by using classifiers that were generated in a local scope, it could be easier for the DCS techniques to select the best one for the difficult samples. For the query samples in easy regions, a simple nearest neighbors rule is used. In the extended version of this work, a deep analysis on the correlation between instance hardness and the performance of DCS techniques is presented. An instance hardness measure that conveys the degree of local class overlap is then used to decide when the local pool is used in the proposed scheme. The proposed method yielded significantly greater recognition rates in comparison to a Bagging-generated pool and two other global pool generation schemes for all DCS techniques evaluated. The proposed scheme's performance was also significantly superior to three state-of-the-art classification models and statistically equivalent to five of them. Moreover, an extended analysis on the computational complexity of the proposed method and of several DS techniques is presented in this version. We also provide the implementation of the proposed technique using the DESLib library on GitHub.