无监督的视频业务特征分析与分类

doi:10.3969/j.issn.0255-8297.2015.02.002

Abstract

Abstract: For recognition of flow statistical features based on machine learning, the key is
to select distinguishable features of different traffic flows. This paper presents several QoSrelated
statistical features that can well discriminate video traffics. To make full use of the
advantages of hierarchical clustering algorithm, this paper uses a flexible feature selection
strategy to mark the network video streaming of different levels. Experiments are performed
on a large scale real network video data. The results show that the proposed method can
achieve significantly higher classification accuracy compared to existing methods.
Keywords: video streaming, statistical features, QoS, traffic classification, hierarchical
clustering

CLC Number:

TP391

YAO Li-tao, DONG Yu-ning. Unsupervised Feature Analysis and Classification of Video Streams[J]. Journal of Applied Sciences, 2015, 33(2): 117-128.

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Unsupervised Feature Analysis and Classification of Video Streams

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