Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1185
Title: | A Fast Hybrid k-NN Classifier Based on Homogeneous Clusters |
Authors: | Ougiaroglou, Stefanos Evangelidis, Georgios |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | Nearest neighbors Classification Clustering |
Issue Date: | 2012 |
Volume: | 381 |
First Page: | 327 |
Last Page: | 336 |
Volume Title: | Artificial Intelligence Applications and Innovations |
Part of Series: | IFIP Advances in Information and Communication Technology |
Part of Series: | IFIP Advances in Information and Communication Technology |
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. |
URI: | https://doi.org/10.1007/978-3-642-33409-2_34 https://ruomo.lib.uom.gr/handle/7000/1185 |
ISBN: | 978-3-642-33408-5 978-3-642-33409-2 |
ISSN: | 1868-4238 1861-2288 |
Other Identifiers: | 10.1007/978-3-642-33409-2_34 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2012_AIAI_openaccess_978-3-642-33409-2_34.pdf | 264,59 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License