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Paper details
Number 1 - March 2021
Volume 31 - 2021
Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network
Dariusz Żelasko, Paweł Pławiak
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.
Keywords
Pay&Require, ensemble learning, machine learning, resource allocation, QoS