Title:  Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule
Internal authors: 
Issue Date:  2004
Journal: 
LECTURE NOTES IN COMPUTER SCIENCE  
Citation:  Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule / L. DIDACI; GIACINTO G. - LNCS 3077(2004), pp. 174-183. ((Intervento presentato al convegno 5th International Workshop on Multiple Classifier Systems, MCS 2004 tenutosi a Cagliari, Italy nel June 2004.
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 parameters
URI:  http://hdl.handle.net/11584/109215
ISBN:  3-540-22144-1
Type: 4.1 Contributo in Atti di convegno

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