Fine-Grained Video Traffic Classification Based on QoE Values

Yang, Lingyun; Yang, Dong; Rana, Md. Sohel; Wang, Zaijian

doi:10.1007/s11277-018-5864-5

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…• CC method selects optimal feature/variable/attribute subset based on correlation function. Selected feature/variable/ attribute should have high correlation degree with label, and low correlation between each other (Yang et al, 2018).…”

Section: Ro1 Analysismentioning

confidence: 99%

“…CC method selects optimal feature/variable/attribute subset based on correlation function. Selected feature/variable/attribute should have high correlation degree with label, and low correlation between each other (Yang et al, 2018). Correlation ranking can only detect linear dependencies between feature/variable/attribute and label (Chandrashekar & Sahin, 2014).…”

Section: Analyzing and Synthesizing Datamentioning

confidence: 99%

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A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

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Quality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space. This article is categorized under: Technologies > Data Preprocessing Algorithmic Development > Multimedia

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Section: Ro1 Analysismentioning

confidence: 99%

Section: Analyzing and Synthesizing Datamentioning

confidence: 99%

A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…In this case, the authors selected features on the basis of the correlation between features and tested the prediction performance under different feature subsets. Yang et al [21] used a consistency-based feature selection method to select the QoE value of videos and the derived statistical features to complete the fine-grained classification of network videos. On the basis of the above work, the existing feature selection methods do not consider the measurement problems of small overlap and no overlap between feature distributions.…”

Section: B Feature Selectionmentioning

confidence: 99%

Video traffic identification with a distribution distance-based feature selection

Liu

Zhang

Sun

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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The boosting of video traffic in recent years has brought severe challenges to Internet management as video traffic shows wide variety, including harmful video traffic. For example, game video traffic should be strictly supervised for teenagers. Therefore, effectively identifying video traffic has become a fundamental issue in network management, and one of the popular and difficult subjects in existing research. To this end, we propose to extract a large-scale feature set for video traffic identification in this paper. Then, a new distribution distance-based feature selection (DDFS) approach is proposed to obtain an effective feature subset. To test the effectiveness of the extracted feature and the DDFS, we designed a video traffic collection architecture to collect different video traffic data. We carried out a set of comparative experiments on the collected dataset, the experimental data suggest that the proposed approach can achieve an accuracy of over 90% for video scene traffic identification and 98% for cloud gaming video traffic identification. Additionally, the comparison of DDFS with four feature selection methods shows that DDFS is a practical feature selection technique for video traffic identification.

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An Optimized K-means Clustering for Improving Accuracy in Traffic Classification

Zhao

Xiao

Ning

et al. 2021

Wireless Pers Commun

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Fine-Grained Video Traffic Classification Based on QoE Values

Cited by 5 publications

References 19 publications

A feature selection for video quality of experience modeling: A systematic literature review

A feature selection for video quality of experience modeling: A systematic literature review

Video traffic identification with a distribution distance-based feature selection

An Optimized K-means Clustering for Improving Accuracy in Traffic Classification

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