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<dc:title xml:lang="pl"><![CDATA[Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network]]></dc:title>
<dc:creator><![CDATA[Żelasko, Dariusz]]></dc:creator>
<dc:creator><![CDATA[Pławiak, Paweł]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[Pay&Require]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[ensemble learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[machine learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[resource allocation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[QoS]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory-high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2021]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86242/AMCS_2021_31_1_10.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101334/edition/86242/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86242]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 31, number 1 (2021)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=119]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101334]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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