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https://ruomo.lib.uom.gr/handle/7000/1205
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Ougiaroglou, Stefanos | - |
dc.contributor.author | Evangelidis, Georgios | - |
dc.contributor.author | Dervos, Dimitris A. | - |
dc.contributor.editor | Corchado, E. | - |
dc.contributor.editor | Snášel, V. | - |
dc.contributor.editor | Abraham, A. | - |
dc.contributor.editor | Woźniak, M. | - |
dc.contributor.editor | Graña, M. | - |
dc.contributor.editor | Cho, SB. | - |
dc.date.accessioned | 2022-08-28T06:18:08Z | - |
dc.date.available | 2022-08-28T06:18:08Z | - |
dc.date.issued | 2012 | - |
dc.identifier | 10.1007/978-3-642-28931-6_16 | en_US |
dc.identifier.isbn | 978-3-642-28930-9 | en_US |
dc.identifier.isbn | 978-3-642-28931-6 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.issn | 1611-3349 | en_US |
dc.identifier.uri | https://doi.org/10.1007/978-3-642-28931-6_16 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1205 | - |
dc.description.abstract | A well known classification method is the k-Nearest Neighbors (k-NN) classifier. However, sequentially searching for the nearest neighbors in large datasets downgrades its performance because of the high computational cost involved. This paper proposes a cluster-based classification model for speeding up the k-NN classifier. The model aims to reduce the cost as much as possible and to maintain the classification accuracy at a high level. It consists of a simple data structure and a hybrid, adaptive algorithm that accesses this structure. Initially, a preprocessing clustering procedure builds the data structure. Then, the proposed algorithm, based on user-defined acceptance criteria, attempts to classify an incoming item using the nearest cluster centroids. Upon failure, the incoming item is classified by searching for the k nearest neighbors within specific clusters. The proposed approach was tested on five real life datasets. The results show that it can be used either to achieve a high accuracy with gains in cost or to reduce the cost at a minimum level with slightly lower accuracy. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science | 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 | k-NN classifier | en_US |
dc.subject.other | cluster-based classification | en_US |
dc.subject.other | data reduction | en_US |
dc.title | An Adaptive Hybrid and Cluster-Based Model for Speeding Up the k-NN Classifier | en_US |
dc.type | Conference Paper | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.volume | 7209 | en_US |
local.identifier.firstpage | 163 | en_US |
local.identifier.lastpage | 175 | en_US |
local.identifier.volumetitle | Hybrid Artificial Intelligent Systems | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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2012_HAIS1.pdf | 291,68 kB | Adobe PDF | Προβολή/Ανοιγμα |
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