Dynamic Selection of Ensembles of Classifiers Using Contextual Information

In a multiple classifier system, dynamic selection (DS) has been used successfully to choose only the best subset of classifiers to recognize the test samples. Dos Santos et al’s approach (DSA) looks very promising in performing DS, since it presents a general solution for a wide range of classifiers. Aiming to improve the performance of DSA, we propose a context-based framework that exploits the internal sources of knowledge embedded in this method. Named

$\mbox{DSA}^{c}$

, the proposed approach takes advantage of the evidences provided by the base classifiers to define the best set of ensembles of classifiers to recognize each test samples, by means of contextual information provided by the validation set. In addition, we propose a switch mechanism to deal with tie-breaking and low-margin decisions. Experiments on two handwriting recognition problems have demonstrated that the proposed approach generally presents better results than DSA, showing the effectiveness of the proposed enhancements. In addition, we demonstrate that the proposed method can be used, without changing the parameters of the base classifiers, in an incremental learning (IL) scenario, suggesting that it is also a promising general IL approach. And the use of a filtering method shows that we can significantly reduce the complexity of

$\mbox{DSA}^{c}$

in the same IL scenario and even resulting in an increase in the final performance.