Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule

Despite the good results provided by Dynamic Classifier Selection (DCS) mechanisms based on local accuracy in a large number of applications, the performances are still capable of improvement. As the selection is performed by computing the accuracy of each classifier in a neighbourhood of the test pattern, performances depend on the shape and size of such a neighbourhood, as well as the local density of the patterns. In this paper, we investigated the use of neighbourhoods; of adaptive shape and size to better cope with the difficulties of a reliable estimation of local accuracies. Reported results show that performance improvements can be achieved by suitably tuning some additional parameters