计算机科学 ›› 2012, Vol. 39 ›› Issue (4): 46-48.
• 计算机网络与信息安全 • 上一篇 下一篇
刘三民,孙知信,刘余霞
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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
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.
Key words: Traffic identification, Support vector machines, K-means, Ensemble learning
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. https://doi.org/
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