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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
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.
ISBN: 9781450307956
Other Identifiers: 10.1145/1995412.1995430
Appears in Collections:Department of Applied Informatics

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