Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1174
Title: | Generating Fixed-Size Training Sets for Large and Streaming Datasets |
Authors: | Ougiaroglou, Stefanos Arampatzis, Georgios Dervos, Dimitris A. Evangelidis, Georgios |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | k-NN Classification Data Reduction Prototype Generation Data Streams Clustering |
Issue Date: | 25-Aug-2017 |
Volume: | 10509 |
First Page: | 88 |
Last Page: | 102 |
Volume Title: | Advances in Databases and Information Systems |
Part of Series: | Lecture Notes in Computer Science |
Part of Series: | Lecture Notes in Computer Science |
Abstract: | The k Nearest Neighbor is a popular and versatile classifier but requires a relatively small training set in order to perform adequately, a prerequisite not satisfiable with the large volumes of training data that are nowadays available from streaming environments. Conventional Data Reduction Techniques that select or generate training prototypes are also inappropriate in such environments. Dynamic RHC (dRHC) is a prototype generation algorithm that can update its condensing set when new training data arrives. However, after repetitive updates, the size of the condensing set may become unpredictably large. This paper proposes dRHC2, a new variation of dRHC, which remedies the aforementioned drawback. dRHC2 keeps the size of the condensing set in a convenient, manageable by the classifier, level by ranking the prototypes and removing the least important ones. dRHC2 is tested on several datasets and the experimental results reveal that it is more efficient and noise tolerant than dRHC and is comparable to dRHC in terms of accuracy. |
URI: | https://doi.org/10.1007/978-3-319-66917-5_7 https://ruomo.lib.uom.gr/handle/7000/1174 |
ISBN: | 978-3-319-66916-8 978-3-319-66917-5 |
ISSN: | 0302-9743 1611-3349 |
Other Identifiers: | 10.1007/978-3-319-66917-5_7 |
Appears in Collections: | Department of Applied Informatics |
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2017_ADBIS.pdf | 315,94 kB | Adobe PDF | View/Open |
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