The assurance of transmission quality in computer networks appears to be an important issue, particularly from the perspective of rapid development of computer networks. In the previous research there were many attempts to implement various Quality of Service (QoS) techniques. Unfortunately, QoS parameters are not always assured - most frequently user pays certain amount of money for transmission parameters which will never be achieved. This paper presents simulation of a new concept, which is determining the transmission quality with the application of Machine Learning (ML). In general, transmission quality is described by means of four parameters, i.e. bandwidth, delay, jitter and packet loss ratio. Pay&Require was suggested as a solution, which allows the assurance of transmission quality in computer networks. This purpose was achieved by the use of multi-agent system which monitors the transmission parameters and checks if they meet the customer’s expectations. The transmission quality rating is a significant factor of Pay&Require. ML was applied in the process of simulation and for the research purpose the assessment system of the transmission quality was implemented. It enabled the test users to assess the quality of transmission. Data obtained in such way was then used in ML classification. Simulations were performed for nine classifiers: Nu-Support Vector Classifier (Nu-SVC), k-Nearest Neighbors algorithm (kNN), Random Forest Classifier, C-Support Vector Classifier (C-SVC), Radius Neighbors Classifier, Nearest Centroid Classifier, Extra Trees Classifier, Decision Tree Classifier and Linear Support Vector Classifier (Linear SVC). Simulations were also performed for two variants of Stacking Classifier. The first variant was a combination of Linear SVC, C-SVC, Nearest Centroid and kNN as estimators and Logistic Regression as the final estimator. In the second variant Random Forest, Extra Trees and kNN were used as estimators and Logistic Regression was applied as the final estimator. The best classification result with respect to the tested data, was achieved by variant 1 Stacking Classifier, it had 89% sensitivity (overall accuracy), with 11/100 incorrect classifications.

特别是从计算机网络的快速发展的角度来看，保证计算机网络的传输质量似乎是一个重要的问题。在先前的研究中，进行了许多尝试以实现各种服务质量（QoS）技术。不幸的是，不能始终保证QoS参数-大多数情况下，用户为传输参数支付一定的费用，而这是永远无法实现的。本文介绍了一个新概念的仿真，该概念正在通过机器学习（ML）的应用确定传输质量。通常，传输质量是通过四个参数来描述的，即带宽，延迟，抖动和丢包率。建议使用“按需付款”作为解决方案，以保证计算机网络中的传输质量。此目的是通过使用多代理系统来实现的，该系统监视传输参数并检查其是否满足客户的期望。传输质量等级是付费与需求的重要因素。在仿真过程中应用了机器学习，并以研究为目的，建立了传输质量评估系统。它使测试用户能够评估传输质量。然后将以此方式获得的数据用于ML分类。针对以下9个分类器进行了仿真：Nu支持向量分类器（Nu-SVC），k最近邻算法（kNN），随机森林分类器，C支持向量分类器（C-SVC），Radius邻居分类器，最近质心分类器，额外树分类器，决策树分类器和线性支持向量分类器（线性SVC）。还对堆叠分类器的两个变体进行了仿真。第一个变量是将线性SVC，C-SVC，最近质心和kNN作为估计量，并将Logistic回归作为最终估计量的组合。在第二个变体“随机森林”中，额外树和kNN被用作估计量，而Logistic回归被用作最终估计量。相对于测试数据，最佳分类结果是通过变体1堆叠分类器获得的，它具有89％的灵敏度（总体准确性），分类错误为11/100。额外树和kNN被用作估计量，逻辑回归被用作最终估计量。相对于测试数据，最佳分类结果是通过变体1堆叠分类器获得的，它具有89％的灵敏度（总体准确性），分类错误为11/100。额外树和kNN被用作估计量，逻辑回归被用作最终估计量。相对于测试数据，最佳分类结果是通过变体1堆叠分类器获得的，它具有89％的灵敏度（总体准确性），分类错误为11/100。