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https://ruomo.lib.uom.gr/handle/7000/1188
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Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Ougiaroglou, Stefanos | - |
dc.contributor.author | Evangelidis, Georgios | - |
dc.contributor.author | Dervos, Dimitris A. | - |
dc.date.accessioned | 2022-08-26T10:38:42Z | - |
dc.date.available | 2022-08-26T10:38:42Z | - |
dc.date.issued | 2011 | - |
dc.identifier | 10.1145/1995412.1995430 | en_US |
dc.identifier.isbn | 9781450307956 | en_US |
dc.identifier.uri | https://doi.org/10.1145/1995412.1995430 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1188 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | FRASCATI::Natural sciences::Computer and information sciences | en_US |
dc.subject.other | classification | en_US |
dc.subject.other | Nearest neighbors | en_US |
dc.subject.other | scalability | en_US |
dc.subject.other | data reduction | en_US |
dc.title | A fast hybrid classification algorithm based on the minimum distance and the k-NN classifiers | en_US |
dc.type | Conference Paper | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.firstpage | 97 | en_US |
local.identifier.volumetitle | Proceedings of the Fourth International Conference on SImilarity Search and APplications - SISAP '11 | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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2011_SISAP.pdf | 339,4 kB | Adobe PDF | Προβολή/Ανοιγμα |
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