Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule
DIDACI, LUCA;GIACINTO, GIORGIO
2004-01-01
Abstract
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 parametersItems in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.