Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network

• Autorzy: Żelasko Dariusz , Pławiak Paweł

Identyfikatory

DOI

10.34768/amcs-2021-0010

• Języki publikacji: EN

Abstrakty

EN

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. 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. 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.

Słowa kluczowe

EN

Pay&Require; ensemble learning; machine learning; resource allocation; QoS

PL

Pay&Require; uczenie zespołowe; uczenie maszynowe; alokacja zasobu; QoS

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 1
• Strony: 135--153
• Opis fizyczny: Bibliogr. 67 poz., rys., tab., wykr.

Twórcy

autor

Żelasko Dariusz

• Department of Information and Communications Technology, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland

autor

Pławiak Paweł

• Department of Information and Communications Technology, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44-100 Gliwice, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).