Network multimedia service semi-supervised classification method based on t distribution hybrid model

Abstract

The invention discloses a network multimedia service semi-supervised classification method based on a t distribution hybrid model. The method concretely includes a step of data set preprocessing, a step of t distribution clustering, and a step of classification. In the step of data set preprocessing, data flow samples of various multimedia services on the Internet are collected and then are preprocessed. In the step of t distribution clustering, fitting of a t distribution hybrid model or a finite t distribution hybrid model is conducted on the above network data flow samples, and K multidimensional t distribution clusters are obtained. In the step of classification, results of the above clustering are further classified, and a total correct rate of the final classification is calculated. The t distribution hybrid model is utilized to conduct more accurate fitting on multimedia services, so the classification accuracy is improved. An EM algorithm of the finite t distribution hybrid model effectively increases the convergence speed of the t distribution hybrid model. An experiment shows that the algorithm is high in accuracy, and the fitting model is superior to a conventional K-means algorithm and a conventional EM algorithm of a Gauss hybrid model.

Description

technical field [0001] The invention belongs to a network flow classification method, in particular to a semi-supervised classification method for network multimedia services based on a t-distribution mixed model. Background technique [0002] In recent years, due to the continuous development of network multimedia services, the difficulty of network traffic monitoring and management and network security has also increased. Classifying and analyzing the current network traffic can help Internet service providers and relevant network management personnel understand the current network status, so as to ensure the quality of service (QoS, Quality of Service) of the network and improve the performance of the computer network. Therefore, network traffic classification has become a research hotspot in current computer networks. [0003] Traffic classification can be mainly divided into four types: port number-based methods, deep packet inspection, statistics-based methods, and be...

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Problems solved by technology

However, due to the influence of outliers in the data samples, the accuracy of these algorithms is low.

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