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| 基于K均值集成和SVM的P2P流量识别研究 | |
| 引用本文： | 刘三民,孙知信,刘余霞.基于K均值集成和SVM的P2P流量识别研究[J].计算机科学,2012,39(4):46-48,74. |
| 作者姓名： | 刘三民 孙知信 刘余霞 |
| 作者单位： | 1. 安徽工程大学计算机与信息学院 芜湖241000;南京航空航天大学计算机科学与技术学院 南京210016 2. 南京航空航天大学计算机科学与技术学院 南京210016;南京邮电大学计算机技术研究所 南京210003;南京大学计算机软件新技术国家重点实验室 南京210093 3. 安徽工程大学电气工程学院 芜湖241000 |
| 基金项目： | 国家自然科学基金，江苏省自然基金，江苏省高校自然科学基础研究项目，安徽工程大学校青年基金项目 |
| 摘 要： | 提出基于K均值集成和支持向量机相结合的P2P流量识别模型,以保证流量识别精度和稳定性,克服聚类识别模型中参数值难以确定、复杂性高等缺点。对少量标签样本采用随机簇中心的K均值算法训练基聚类器,按最大后验概率分配簇标签,无标签样本与其最近簇标签一致;按投票机制集成无标签样本标签信息,并结合原标签样本训练支持向量机识别模型。该模型利用了集成学习稳定性和SVM在小样本集上的良好泛化性能。理论分析和仿真实验结果证明了方案的可行性。 |
| 关 键 词： | 流量识别 支持向量机 K均值 集成学习 |
Research on P2P Traffic Identification Based on K-means Ensemble and SVM |
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| LIU San-min , SUN Zhi-xin , LIU Yu-xia.Research on P2P Traffic Identification Based on K-means Ensemble and SVM[J].Computer Science,2012,39(4):46-48,74. | |
| Authors: | LIU San-min SUN Zhi-xin LIU Yu-xia |
| Affiliation: | 5(College of Computer and Information,Anhui Polytechnic University,Wuhu 241000,China)1(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)2(College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)3(State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093,China)4(College of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China)5 |
| Abstract: | A P2P traffic identification model was constructed by the combination of K-means ensemble and support vector machine. It owns high accuracy, stability and overcomes complexity of cluster model. Firstly, the three base clusterer was formed by few labeled sample, and then the each cluster's label was assigned by MAP. The unlabeled sample's label is the same with the closest cluster. Identification model based on SVM was built by new sample set. hhe model makes the best of ensemble learning's stability and SVM's generalization ability, theoretical analysis and result demon-strate its feasibility. |
| Keywords: | Traffic identification Support vector machines K-means Ensemble learning |
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