计算机工程与应用2018，Vol.54Issue(10)：31-38,8.DOI:10.3778/j.issn.1002-8331.1801-0429
基于t分布混合模型的半监督网络流分类方法
Semi-supervised network traffic classification based on t-distribution mixture model
摘要
Abstract
Traditional Gaussian distribution is susceptible to the influence of edge points and outliers in data samples, therefore student's t-distribution is adopted to improve Gaussian distribution.The EM(Expectation Maximization)algo-rithm is used to build T-distribution Mixture Model(TMM)for network multimedia traffic dataset.Then,A new Limited T-distribution Mixture Model(LTMM)is presented to reduce the number of iterations for EM algorithm,whose effective-ness is demonstrated by theoretical analysis and experiments.The flows of multimedia services are classified in this paper. Experiments show that the proposed algorithm can achieve higher accuracy than existing methods and the fitted model is better than K-Means algorithm and EM algorithm for Gaussian mixture model.关键词
网络流分类/t分布混合模型/期望最大化算法/半监督分类Key words
internet traffic classification/t-distribution mixture model/Expectation Maximization(EM)algorithm/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
董育宁,朱善胜,赵家杰..基于t分布混合模型的半监督网络流分类方法[J].计算机工程与应用,2018,54(10):31-38,8.基金项目
国家自然科学基金(No.61271233) （No.61271233）
华为HIRP创新项目. （）
