This paper proposes a hybrid method for fast and accurate Nearest Neighbor Classification. The method consists of a non-parametric cluster-based algorithm that produces a two-level speed-up data structure and a hybrid algorithm that accesses this structure to perform the classification. The proposed method was evaluated using eight real-life datasets and compared to four known speed-up methods. Experimental results show that the proposed method is fast and accurate, and, in addition, has low pre-processing computational cost. © 2012 IFIP International Federation for Information Processing.
CITATION STYLE
Ougiaroglou, S., & Evangelidis, G. (2012). A fast hybrid k-NN classifier based on homogeneous clusters. In IFIP Advances in Information and Communication Technology (Vol. 381 AICT, pp. 327–336). https://doi.org/10.1007/978-3-642-33409-2_34
Mendeley helps you to discover research relevant for your work.