Dynamic selection approaches for multiple classifier systems
Dynamic selection approaches for multiple classifier systems
Cavalin, Paulo R. and Sabourin, Robert and Suen, Ching Y.
Neural Computing and Applications 2013
Abstract : 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 define a multi-layer fusion function adapted to each recognition problem, we propose dynamic multistage organization (DMO), which defines the best multistage structure for each test sample. By extending Dos Santos et al.’s approach, we propose two implementations for DMO, namely DSAm and DSAc. While the former considers a set of dynamic selection functions to generalize a DMO structure, the latter considers contextual information, represented by the output profiles computed from the validation dataset, to conduct this task. The experimental evaluation, considering both small and large datasets, demonstrated that DSAc dominated DSAm on most problems, showing that the use of contextual information can reach better performance than other existing methods. In addition, the performance of DSAc can also be enhanced in incremental learning. However, the most important observation, supported by additional experiments, is that dynamic selection is generally preferred over static approaches when the recognition problem presents a high level of uncertainty. © 2011 Springer-Verlag London Limited.
