Dynamic selection of classifiers—A comprehensive review

• Published in: Pattern recognition 2014-11, Vol.47 (11), p.3665-3680
• Main Authors: Britto, Alceu S., Sabourin, Robert, Oliveira, Luiz E.S.
• Format: Article
• Language: English
• Subjects: Applied sciences; Classification; Classifiers; Complexity; Data complexity; Dynamic selection of classifiers; Dynamic tests; Dynamical systems; Dynamics; Ensemble of classifiers; Exact sciences and technology; Information, signal and communications theory; Literature reviews; Pattern recognition; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2014.05.003

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 such a classification approach and then it presents the state of the art organized according to a proposed taxonomy. In addition, a two-step analysis is applied to the results of the main methods reported in the literature, considering different classification problems. The first step is based on statistical analyses of the significance of these results. The idea is to figure out the problems for which a significant contribution can be observed in terms of classification performance by using a dynamic selection approach. The second step, based on data complexity measures, is used to investigate whether or not a relation exists between the possible performance contribution and the complexity of the classification problem. From this comprehensive study, we observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier. In addition, we found evidence of a relation between the observed performance contribution and the complexity of the classification problem. These observations allow us to suggest, from the classification problem complexity, that further work should be done to predict whether or not to use a dynamic selection approach. •We review the main methods of dynamic selection of classifiers.•A taxonomy for the methods of dynamic selection of classifiers is proposed.•We examine the significance of the reported results on dynamic selection.•The performance of dynamic selection methods is related to the problem complexity.•We investigate whether or not the dynamic selection of classifiers should be used.