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|Title:||A fast hybrid classification algorithm based on the minimum distance and the k-NN classifiers|
Dervos, Dimitris A.
|Subjects:||FRASCATI::Natural sciences::Computer and information sciences|
|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.|
|Appears in Collections:||Department of Applied Informatics |
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