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2004
Springer

Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule

9 years 2 months ago
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.
Luca Didaci, Giorgio Giacinto
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where MCS
Authors Luca Didaci, Giorgio Giacinto
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