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Title: | A fast hybrid classification algorithm based on the minimum distance and the k-NN classifiers |
Authors: | Ougiaroglou, Stefanos Evangelidis, Georgios Dervos, Dimitris A. |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | classification Nearest neighbors scalability data reduction |
Issue Date: | 2011 |
First Page: | 97 |
Volume Title: | Proceedings of the Fourth International Conference on SImilarity Search and APplications - SISAP '11 |
Abstract: | Some of the most commonly used classifiers are based on the retrieval and examination of the k Nearest Neighbors of unclassified instances. However, since the size of datasets can be large, these classifiers are inapplicable when the time-costly sequential search over all instances is used to find the neighbors. The Minimum Distance Classifier is a very fast classification approach but it usually achieves much lower classification accuracy than the k-NN classifier. In this paper, a fast, hybrid and model-free classification algorithm is introduced that combines the Minimum Distance and the k-NN classifiers. The proposed algorithm aims at maximizing the reduction of computational cost, by keeping classification accuracy at a high level. The experimental results illustrate that the proposed approach can be applicable in dynamic, time-constrained environments. |
URI: | https://doi.org/10.1145/1995412.1995430 https://ruomo.lib.uom.gr/handle/7000/1188 |
ISBN: | 9781450307956 |
Other Identifiers: | 10.1145/1995412.1995430 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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2011_SISAP.pdf | 339,4 kB | Adobe PDF | View/Open |
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