Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

This work presents a literature review of multiple classifier systems based on the dynamic
selection of classifiers. First, it briefly reviews some basic concepts and definitions related to …

In this paper we propose a new approach for dynamic selection of ensembles of classifiers.
Based on the concept named multistage organizations, the main objective of which is to …

Dynamic classifier selection (DCS) plays a strategic role in the field of multiple classifier
systems (MCS). This paper proposes a study on the performances of DCS by Local …

Abstract Dynamic Ensemble Selection (DES) techniques aim to select only the most
competent classifiers for the classification of each test sample. The key issue in DES is how …

The overproduce-and-choose strategy, which is divided into the overproduction and
selection phases, has traditionally focused on finding the most accurate subset of classifiers …

DESlib is an open-source python library providing the implementation of several dynamic
selection techniques. The library is divided into three modules:(i) dcs, containing the …

Dynamic ensemble selection (DES) techniques work by estimating the competence level of
each classifier from a pool of classifiers, and selecting only the most competent ones for the …

Abstract The One-vs-One strategy is one of the most commonly used decomposition
technique to overcome multi-class classification problems; this way, multi-class problems …

Ensemble of classifiers is an effective way of improving performance of individual classifiers.
However, the task of selecting the ensemble members is often a non-trivial one. For …