A Fast Hybrid k-NN Classifier Based on Homogeneous Clusters

Abstract : 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.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.327-336, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_34〉
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Stefanos Ougiaroglou, Georgios Evangelidis. A Fast Hybrid k-NN Classifier Based on Homogeneous Clusters. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.327-336, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_34〉. 〈hal-01521434〉

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