Experimental evaluation of selected tree structures for exact and approximate k-nearest neighbor classification
Aleksander Cisłak, Szymon Grabowski
DOI: http://dx.doi.org/10.15439/2014F194
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 93–100 (2014)
Abstract. Spatial data structures, for vector or metric spaces, are a well-known means to speed-up proximity queries. One of the common uses of the found neighbors of the query object is in classification methods, e.g, the famous \emph{k}-nearest neighbor algorithm. Still, most experimental works focus on providing attractive tradeoffs between neighbor search times and the neighborhood quality, but they ignore the impact of such tradeoffs on the classification accuracy.