<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Department of Information and Communications Technology

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

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...

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

Publié en ligne: 03 avr. 2021

Pages: 135 - 153

Reçu: 26 juin 2020

Accepté: 06 nov. 2020

DOI: https://doi.org/10.34768/amcs-2021-0010

Mots clés
Pay&Require, ensemble learning, machine learning, resource allocation, QoS

© 2021 Dariusz Żelasko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Publié en ligne: 03 avr. 2021

Pages: 135 - 153

Reçu: 26 juin 2020

Accepté: 06 nov. 2020

DOI: https://doi.org/10.34768/amcs-2021-0010

Mots clésPay&Require, ensemble learning, machine learning, resource allocation, QoS

© 2021 Dariusz Żelasko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Pay&Require, ensemble learning, machine learning, resource allocation, QoS