Skip to Main content Skip to Navigation
New interface
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, May 11, 2017 - 5:10:49 PM
Last modification on : Tuesday, January 5, 2021 - 5:20:01 PM
Long-term archiving on: : Saturday, August 12, 2017 - 2:01:21 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Stefanos Ougiaroglou, Georgios Evangelidis. A Fast Hybrid k-NN Classifier Based on Homogeneous Clusters. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.327-336, ⟨10.1007/978-3-642-33409-2_34⟩. ⟨hal-01521434⟩



Record views


Files downloads