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

A multimedia business, distributed hybrid technology, applied in data exchange network, character and pattern recognition, instruments and other directions, can solve the problem of low algorithm accuracy, reduce the number of iterations, data fitting model is accurate, improve reliability and effectiveness sexual effect

Active Publication Date: 2017-06-20
NANJING UNIV OF POSTS & TELECOMM
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However, due to the influence of outliers in the da

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

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[0051] The present invention will be described in further detail in conjunction with the accompanying drawings.

[0052] The data set of network traffic distribution is often measured by QoS characteristics, including data packet size, data packet transmission interval, etc. In order to measure the distribution of data samples, a Gaussian Mixture Model (GMM) can be introduced to fit the samples. The t distribution can be seen as an extension of the Gaussian distribution. Due to its "long tail" characteristics, it can more accurately fit the distribution of data samples. Therefore, the data samples can be further fitted with a t-distribution mixed model (TMM).

[0053] For the Gaussian distribution, there is a 3σ criterion for the data sample, that is, if the value of the data sample outside the confidence interval (μ-3σ, μ+3σ) is less than 0.3%, the sample can be considered as a noise point. Due to the influence of degrees of freedom in the t distribution, the confidence in...

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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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Application Information

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IPC IPC(8): H04L12/24H04L12/26G06K9/62
CPCH04L41/145H04L43/0876G06F18/2321
Inventor 董育宁赵家杰
Owner NANJING UNIV OF POSTS & TELECOMM
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