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.
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Didaci, L., Giacinto, G. (2004). Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_17
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DOI: https://doi.org/10.1007/978-3-540-25966-4_17
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