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2015 International Symposium on Computer Science and Software Engineering (CSSE)
the k-nearest neighbor (k-NN) is a widely used classification technique and has significant applications in various domains. The most challenging issues in the k-nearest neighbor algorithm are high dimensional data, the reasonable accuracy of results and suitable computation time. Nowadays, using parallel processing and deploying many-core platforms like GPUs is considered as one of the popular approaches to improving these issues. In this paper, we present a novel and accurate paralleldoi:10.1109/csicsse.2015.7369240 fatcat:7itafox5zzhavmvnd657q6di6e