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Frontiers of Computer Science

, Volume 12, Issue 6, pp 1060–1075 | Cite as

A survey of data-driven approach on multimedia QoE evaluation

  • Ruochen Huang
  • Xin Wei
  • Liang ZhouEmail author
  • Chaoping Lv
  • Hao Meng
  • Jiefeng Jin
Review Article

Abstract

With the development of mobile communication technology and the growth of mobile device, the requirements for user quality of experience (QoE) become higher and higher. Network operators and content providers are interested in QoE evaluation for improving users’ QoE. However, multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE. In this paper, we provide a survey of the state of the art about applying data-driven approach on QoE evaluation. Firstly, we describe the way to choose factors influencing QoE. Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users’ QoE. Finally, we describe our research work on how to evaluate QoE in imbalanced dataset.

Keywords

quality of experience data-driven machine learning imbalanced dataset 

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Notes

Acknowledgements

This work is partly supported by the State Key Development Program of Basic Research of China (2013CB329005), the National Natural Science Foundation of China (Grant Nos. 61401227, 61571240), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Natural Science Foundation of Jiangsu Province (BK20161517), the Qing Lan Project, the Scientific Research, Foundation of NUPT (NY217022), the Postdoctoral Science Foundation of China (2017M611881).

Supplementary material

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Supplementary material, approximately 248 KB.

References

  1. 1.
    Cisco V N I. Global mobile data traffic forecast update, 2015–2020, White Paper Document ID, 2016, 958959758Google Scholar
  2. 2.
    Index C U N. Cisco visual networking index: global mobile data traffic forecast 2014–2019. Technical Report, 2015Google Scholar
  3. 3.
    ITU-T Recommendation P.10/G.100, Vocabulary for performance and quality of service. Amendment 2: New definitions for inclusion in Recommendation ITU-T P.10/G.100, Int. Telecomm. Union, Geneva 2008.Google Scholar
  4. 4.
    ETSI Technical Report 102 643 V1.0.2, Human Factors (HF); Quality of Experience (QoE) requirements for real-time communication services, 2010Google Scholar
  5. 5.
    Yamagishi K, Hayashi T. Parametric packet-layer model for monitoring video quality of IPTV services. In: Proceedings of 2008 IEEE International Conference on Communications. 2008, 110–114Google Scholar
  6. 6.
    Belmudez B, Möller S. Extension of the G.1070 video quality function for the MPEG2 video codec. In: Proceedings of the 2nd International Workshop on Quality of Multimedia Experience. 2010, 7–10Google Scholar
  7. 7.
    You F H, Zhang W, Xiao J. Packet loss pattern and parametric video quality model for IPTV. In: Proceedings of the 8th IEEE/ACIS International Conference on Computer and Information Science. 2009, 824–828Google Scholar
  8. 8.
    Wang Z, Li L, Wang W, Wan Z, Fang Y, Cai C. A study on QoS/QoE correlation model in wireless-network. In: Proceedings of Signal and Information Processing Association Annual Summit and Conference. 2014, 1–6Google Scholar
  9. 9.
    Kim H J, Lee D H, Lee J M, Lee K H, Lyu W, Choi S G. The QoE evaluation method through the QoS-QoE correlation model. In: Proceedings of Networked Computing and Advanced Information Management. 2008, 719–725Google Scholar
  10. 10.
    Paudyal P, Battisti F, Carli M. Impact of video content and transmission impairments on quality of experience. Multimedia Tools and Applications, 2016, 75(23): 16461–16485Google Scholar
  11. 11.
    Kim J, Um T W, Ryu W, Lee B S, Hahn M. Iptv systems, standards and architectures: part ii-heterogeneous networks and terminalaware QoS/QoE-guaranteed mobile iptv service. IEEE Communications Magazine, 2008, 46(5): 110–117Google Scholar
  12. 12.
    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600–612Google Scholar
  13. 13.
    Khorsandroo S, Md Noor R, Khorsandroo S. A generic quantitative relationship to assess interdependency of QoE and QoS. KSII Transactions on Internet and Information Systems, 2013, 7(2): 327–346Google Scholar
  14. 14.
    Reichl P, Egger S, Schatz R, D’alconzo A. The logarithmic nature of QoE and the role of the weber-fechner law in QoE assessment. In: Proceedings of 2010 IEEE International Conference on Communications. 2010, 1–5Google Scholar
  15. 15.
    Reichl P, Tuffin B, Schatz R. Logarithmic laws in service quality perception: where microeconomics meets psychophysics and quality of experience. Telecommunication Systems, 2013, 52(2): 587–600Google Scholar
  16. 16.
    Hannuksela M M. Does context matter in quality evaluation of mobile television? In: Proceedings of International Conference on Human Computer Interaction with Mobile Devices and Services. 2008, 63–72Google Scholar
  17. 17.
    Kaikkonen A, Kekäläinen A, Cankar M, Kallio T, Kankainen A. Usability testing of mobile applications: a comparison between laboratory and field testing. Journal of Usability Studies, 2005, 1(1): 4–16Google Scholar
  18. 18.
    Kjeldskov J, Stage J. New techniques for usability evaluation of mobile systems. International Journal of Human-Computer Studies, 2004, 60(5–6): 599–620Google Scholar
  19. 19.
    Machajdik J, Hanbury A, Garz A, Sablatnig R. Affective computing for wearable diary and lifelogging systems: an overview. In: Proceedings of the Workshop of the Austrian Association for Pattern Recognition. 2011, 2447–2456Google Scholar
  20. 20.
    Hamm J, Stone B, Belkin M, Dennis S. Automatic annotation of daily activity from smartphone-based multisensory streams. In: Proceedings of the International Conference on Mobile Computing, Applications, and Services. 2012, 328–342Google Scholar
  21. 21.
    Jalal A, Uddin M Z, Kim T S. Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home. IEEE Transactions on Consumer Electronics, 2012, 58(3): 863–871Google Scholar
  22. 22.
    Kamal S, Jalal A. A hybrid feature extraction approach for human detection, tracking and activity recognition using depth sensors. Arabian Journal for Science and Engineering, 2016, 41(3): 1043–1051Google Scholar
  23. 23.
    Jalal A, Kamal S, Kim D. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors, 2014, 14(7): 11735–11759Google Scholar
  24. 24.
    Jalal A, Sarif N, Kim J T, Kim T S. Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home. Indoor and Built Environment, 2013, 22(1): 271–279Google Scholar
  25. 25.
    Farooq A, Jalal A, Kamal S. Dense rgb-d map-based human tracking and activity recognition using skin joints features and self-organizing map. Ksii Transactions on Internet and Information Systems, 2015, 9(5): 1856–1869Google Scholar
  26. 26.
    Jalal A, Kim Y, Kim D. Ridge body parts features for human pose estimation and recognition from rgb-d video data. In: Proceedings of International Conference on Computing, Communication and Networking Technologies. 2014, 1–6Google Scholar
  27. 27.
    Jalal A, Kim J T, Kim T S. Development of a life logging system via depth imaging-based human activity recognition for smart homes. In: Proceedings of the International Symposium on Sustainable Healthy Buildings. 2012, 91–104Google Scholar
  28. 28.
    Jalal A, Lee S, Kim J T, Kim T S. Human activity recognition via the features of labeled depth body parts. In: Proceedings of International Conference on Smart Homes and Health Telematics. 2012, 246–249Google Scholar
  29. 29.
    Baraković S, Skorin-Kapov L. Survey and challenges of QoE management issues in wireless networks. Journal of Computer Networks and Communications, 2013: 1–28Google Scholar
  30. 30.
    He Z, Mao S, Jiang T. A survey of QoE-driven video streaming over cognitive radio networks. IEEE Network, 2015, 29(6): 20–25Google Scholar
  31. 31.
    Tang W, Nguyen T D, Huh E N. A survey study on QoE perspective of mobile cloud computing. In: Proceedings of Information Science and Applications. 2014, 1–4Google Scholar
  32. 32.
    Mitra K, Zaslavsky A, hlund C. QoE modelling, measurement and prediction: a review. 2014, arXiv preprint arXiv:1410.6952Google Scholar
  33. 33.
    Seufert M, Egger S, Slanina M, Zinner T, Hossfeld T, Tran-Gia P. A survey on quality of experience of http adaptive streaming. IEEE Communications Surveys and Tutorials, 2015, 17(1): 469–492Google Scholar
  34. 34.
    Alreshoodi M, Woods J. Survey on QoE№S correlation models for multimedia services. International Journal of Distributed and Parallel Systems, 2013, 4(3): 53–72Google Scholar
  35. 35.
    Yun L, Peng Z. An automatic hand gesture recognition system based on viola-jones method and SVMS. In: Proceedings of the 2nd International Workshop on Computer Science and Engineering. 2009, 72–76Google Scholar
  36. 36.
    Kamal S, Jalal A, Kim D. Depth images-based human detection, tracking and activity recognition using spatiotemporal features and modified hmm. Journal of Electrical Engineering and Technology, 2016, 11(3): 1921–1926Google Scholar
  37. 37.
    Yamauchi K, Chen W, Wei D. 3D mobile phone applications in telemedicine-a survey. In: Proceedings of the 5th International Conference on Computer and Information Technology. 2005, 956–960Google Scholar
  38. 38.
    Jalal A, Uddin I. Security architecture for third generation (3g) using gmhs cellular network. In: Proceedings of International Conference on Emerging Technologies. 2007, 74–79Google Scholar
  39. 39.
    Jalal A, Zeb M A. Security and QoS optimization for distributed real time environment. In: Proceedings of the IEEE International Conference on Computer and Information Technology. 2007, 369–374Google Scholar
  40. 40.
    Jalal A, Rasheed Y A. Collaboration achievement along with performance maintenance in video streaming. In: Proceedings of the IEEE Conference on Interactive Computer Aided Learning. 2007, 1–8Google Scholar
  41. 41.
    Jalal A, Zeb M A. Security enhancement for e-learning portal. International Journal of Computer Science and Network Security, 2008, 8(3): 41–45Google Scholar
  42. 42.
    Jalal A, Kim S, Yun B. Assembled algorithm in the real-time h. 263 codec for advanced performance. In: Proceedings of Enterprise Networking and Computing in Healthcare Industry. 2005, 295–298Google Scholar
  43. 43.
    Jalal A, Kim S. Advanced performance achievement using multialgorithmic approach of video transcoder for low bit rate wireless communication. International Journal on Graphics, Vision and Image Processing, 2005, 5(9): 27–32Google Scholar
  44. 44.
    Jalal A, Sarif N, Kim J T, Kim T S. Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home. Indoor and Built Environment, 2013, 22(1): 271–279Google Scholar
  45. 45.
    Jalal A, Kim S. A complexity removal in the floating point and rate control phenomenon. In: Proceedings of the Conference on Korea Multimedia Society. 2005, 48–51Google Scholar
  46. 46.
    Jalal A, Kim S. Global security using human face understanding under vision ubiquitous architecture system. Enformatika, 2011, 13: 7–11Google Scholar
  47. 47.
    Jalal A, Kim S. Algorithmic implementation and efficiency maintenance of real-time environment using low-bitrate wireless communication. In: Proceedings of IEEEWorkshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the 2nd International Workshop on Collaborative Computing, Integration, and Assurance. 2006, 81–88Google Scholar
  48. 48.
    Zhou L. QoE-driven delay announcement for cloud mobile media. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(1): 84–94Google Scholar
  49. 49.
    Zhou L. On data-driven delay estimation for media cloud. IEEE Transactions on Multimedia, 2016, 18(5): 905–915Google Scholar
  50. 50.
    Chen Y, Wu K, Zhang Q. From QoS to QoE: a tutorial on video quality assessment. IEEE Communications Surveys and Tutorials, 2015, 17(2): 1126–1165Google Scholar
  51. 51.
    Baraković S, Baraković J, Bajrić H. QoE dimensions and QoE measurement of NGN services. In: Proceedings of the 18th Telecommunications Forum. 2010, 15–18Google Scholar
  52. 52.
    Skorin-Kapov L, Varela M. A multi-dimensional view of QoE: the arcu model. In: Proceedings of the 35th International Convention. 2012, 662–666Google Scholar
  53. 53.
    Gill P, Arlitt M, Li Z, Mahanti A. Youtube traffic characterization: a view from the edge. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. 2007, 15–28Google Scholar
  54. 54.
    Brunnström K, Beker S A, De Moor K, Dooms A, Egger S, Garcia MN, Hossfeld T, Jumisko-Pyykkö S, Keimel C, Larabi M-C. Qualinet white paper on definitions of quality of experience. In: Proceedings of Qualinet White Paper on Definitions of Quality of Experience Output from the 5th Qualinet Meeting. 2013Google Scholar
  55. 55.
    Piyathilaka L, Kodagoda S. Gaussian mixture based hmm for human daily activity recognition using 3D skeleton features. In: Proceedings of the 8th IEEE Conference on Industrial Electronics and Applications. 2013, 567–572Google Scholar
  56. 56.
    Jalal A, Kamal S, Kim D. Depth map-based human activity tracking and recognition using body joints features and self-organized map. In: Proceedings of International Conference on Computing, Communication and Networking Technologies. 2014, 1–6Google Scholar
  57. 57.
    Jalal A, Kim Y. Dense depth maps-based human pose tracking and recognition in dynamic scenes using ridge data. In: Proceedings of the International Conference on Advanced Video and Signal Based Surveillance. 2014, 119–124Google Scholar
  58. 58.
    Jalal A, Kim J T, Kim T S. Human activity recognition using the labeled depth body parts information of depth silhouettes. In: Proceedings of the 6th International Symposium on Sustainable Healthy Buildings. 2012, 1–8Google Scholar
  59. 59.
    Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistics Surveys, 2010, 4: 40–79MathSciNetzbMATHGoogle Scholar
  60. 60.
    Kim J H. Estimating classification error rate: repeated crossvalidation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis, 2009, 53(11): 3735–3745MathSciNetzbMATHGoogle Scholar
  61. 61.
    Dobrian F, Awan A, Zhan J, Zhang H. Understanding the impact of video quality on user engagement. ACM SIGCOMMComputer Communication Review, 2011, 41(4): 362–373Google Scholar
  62. 62.
    Balachandran A, Sekar V, Akella A, Seshan S, Stoica I, Zhang H. Developing a predictive model of quality of experience for internet video. ACM SIGCOMM Computer Communication Review, 2013, 43(4): 339–350Google Scholar
  63. 63.
    Zhang Y, Yue T, Wang H, Wei A. Predicting the quality of experience for internet video with fuzzy decision tree. In: Proceedings of the 17th International Conference on Computational Science and Engineering. 2014, 1181–1187Google Scholar
  64. 64.
    Menkovski V, Exarchakos G, Liotta A. Online QoE prediction. In: Proceedings of the 2nd International Workshop on Quality of Multimedia Experience. 2010, 118–123Google Scholar
  65. 65.
    Menkovski V, Exarchakos G, Liotta A. Online learning for quality of experience management. In: Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands. 2010Google Scholar
  66. 66.
    Zheng K, Zhang X, Zheng Q, Xiang W. Quality-of-experience assessment and its application to video services in LTE networks. IEEE Wireless Communications, 2015, 22(1): 70–78Google Scholar
  67. 67.
    Paudel I, Pokhrel J, Wehbi B, Cavalli A, Jouaber B. Estimation of video QoE from mac parameters in wireless network: a random neural network approach. In: Proceedings of the International Symposium on Communications and Information Technologies. 2015, 51–55Google Scholar
  68. 68.
    Kapa M, Happe L, Jakab F. Prediction of quality of user experience for video streaming over IP networks. Cyber Journals, 2012: 22–35Google Scholar
  69. 69.
    Mushtaq M S, Augustin B, Mellouk A. Empirical study based on machine learning approach to assess the QoS/QoE correlation. In: Proceedings of the 17th European Conference on Networks and Optical Communications. 2012, 1–7Google Scholar
  70. 70.
    Chen H, Yu X, Xie L. End-to-end quality adaptation scheme based on QoE prediction for video streaming service in lte networks. In: Proceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. 2013, 627–633Google Scholar
  71. 71.
    Qian L, Chen H, Xie L. SVM-based QoE estimation model for video streaming service over wireless networks. In: Proceedings of the International Conference on Wireless Communications and Signal Processing. 2015, 1–6Google Scholar
  72. 72.
    Kang Y, Chen H, Xie L. An artificial-neural-network-based QoE estimation model for video streaming over wireless networks. In: Proceedings of the International Conference on Communications in China. 2013, 264–269Google Scholar
  73. 73.
    De Pessemier T, De Moor K, Joseph W, De Marez L, Martens L. Quantifying the influence of rebuffering interruptions on the user’s quality of experience during mobile video watching. IEEE Transactions on Broadcasting, 2013, 59(1): 47–61Google Scholar
  74. 74.
    Aguiar E, Riker A, Cerqueira E, Abelém A, Mu M, Braun T, Curado M, Zeadally S. A real-time video quality estimator for emerging wireless multimedia systems. Wireless Networks, 2014, 20(7): 1759–1776Google Scholar
  75. 75.
    Chen Y, Chen Q, Zhang F, Zhang Q, Wu K, Huang R, Zhou L. Understanding viewer engagement of video service in wi-fi network. Computer Networks, 2015, 91: 101–116Google Scholar
  76. 76.
    Krishnan S S, Sitaraman R K. Video stream quality impacts viewer behavior: Inferring causality using quasi-experimental designs. IEEE/ACM Transactions on Networking, 2013, 21(6): 2001–2014Google Scholar
  77. 77.
    Hsu C W, Lin C J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415–425Google Scholar
  78. 78.
    Wang B, Zou D, Ding R. Support vector regression based video quality prediction. In: Proceedings of the International Symposium on Multimedia. 2011, 476–481Google Scholar
  79. 79.
    Andrews R, Diederich J, Tickle A B. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based Systems, 1995, 8(6): 373–389zbMATHGoogle Scholar
  80. 80.
    Marchette D J. Bayesian networks and decision graphs. Technometrics, 2008, 45(2): 178–179Google Scholar
  81. 81.
    Mitra K, Zaslavsky A, Hlund C. Dynamic bayesian networks for sequential quality of experience modelling and measurement. In: Proceedings of International Conference on Smart Spaces and Next Generation Wired/wireless Networking. 2011, 135–146Google Scholar
  82. 82.
    Mian A U, Hu Z, Tian H. Estimation of in-service quality of experience for peer-to-peer live video streaming systems using a usercentric and context-aware approach based on bayesian networks. Transactions on Emerging Telecommunications Technologies, 2013, 24(3): 280–287Google Scholar
  83. 83.
    Rabiner L R. A tutorial on hidden markov models and selected applications in speech recognition. Readings in Speech Recognition, 1990, 77(2): 267–296Google Scholar
  84. 84.
    Jalal A, Kim Y, Kamal S, Farooq A, Kim D. Human daily activity recognition with joints plus body features representation using kinect sensor. In: Proceedings of the International Conference on Informatics, Electronics and Vision. 2015, 1–6Google Scholar
  85. 85.
    Jalal A, Kamal S, Kim D. Human depth sensors-based activity recognition using spatiotemporal features and hidden markov model for smart environments. Journal of Computer Networks and Communications, 2016(17): 1–11Google Scholar
  86. 86.
    Jalal A, Kamal S, Kim D. Individual detection-tracking-recognition using depth activity images. In: Proceedings of the International Conference on Ubiquitous Robots and Ambient Intelligence. 2015, 450–455Google Scholar
  87. 87.
    Jalal A, Kamal S. Real-time life logging via a depth silhouette-based human activity recognition system for smart home services. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance. 2014, 74–80Google Scholar
  88. 88.
    Jalal A, Kim Y H, Kim Y J, Kamal S, Kim D. Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognition, 2016, 61: 295–308Google Scholar
  89. 89.
    Jalal A, Kamal S, Kim D. Shape and motion features approach for activity tracking and recognition from kinect video camera. In: Proceedings of the IEEE International Conference on Advanced Information networking and Applications Workshops. 2015, 445–450Google Scholar
  90. 90.
    Min C, Hao Y, Mao S, Di W, Yuan Z. User intent-oriented video QoE with emotion detection networking. In: Proceedings of Global Communications Conference. 2017, 1–6Google Scholar
  91. 91.
    Sun Y, Yin X, Wang N, Jiang J, Sekar V, Jin Y, Sinopoli B. Analyzing TCP throughput stability and predictability with implications for adaptive video streaming. 2015, arXiv preprint arXiv: 1506.05541Google Scholar
  92. 92.
    Mitra K, Hlund C, Zaslavsky A. QoE estimation and prediction using hidden markov models in heterogeneous access networks. In: Proceedings of Telecommunication Networks and Applications Conference. 2012, 1–5Google Scholar
  93. 93.
    Hoβfeld T, Biedermann S, Schatz R, Platzer A, Egger S, Fiedler M. The memory effect and its implications on web QoE modeling. In: Proceedings of the 23rd International on Teletraffic Congress. 2011, 103–110Google Scholar
  94. 94.
    Tasaka S. A bayesian hierarchical model of QoE in interactive audiovisual communications. In: Proceedings of the IEEE International Conference on Communications. 2015, 6983–6989Google Scholar
  95. 95.
    Charonyktakis P, Plakia M, Tsamardinos I, Papadopouli M. On usercentric modular QoE prediction for voip based on machine-learning algorithms. IEEE Transactions on Mobile Computing, 2016, 15(6): 1443–1456Google Scholar
  96. 96.
    Venkataraman M, Chatterjee M, Chattopadhyay S. Evaluating quality of experience for streaming video in real time. In: Proceedings of IEEE Global Telecommunications Conference. 2009, 1–6Google Scholar
  97. 97.
    Zhou Z H. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6–16Google Scholar
  98. 98.
    He H, Garcia E A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263–1284Google Scholar
  99. 99.
    Kubat M, Holte R C, Matwin S. Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 1998, 30(2-3): 195–215Google Scholar
  100. 100.
    He H, Shen X. A ranked subspace learning method for gene expression data classification. In: Proceedings of the International Conference on Artificial Intelligence. 2007, 358–364Google Scholar
  101. 101.
    Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16: 321–357zbMATHGoogle Scholar
  102. 102.
    López V, Fernández A, García S, Palade V, Herrera F. An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Information Sciences, 2013, 250: 113–141Google Scholar
  103. 103.
    Ramyachitra D, Manikandan P. Imbalanced dataset classification and solutions: a review. International Journal of Computing and Business Research, 2014, 5(4): 1–29Google Scholar
  104. 104.
    Wang L, Jin J, Huang R, Wei X, Chen J. Unbiased decision tree model for user’s QoE in imbalanced dataset. In: Proceedings of the International Conference on Cloud Computing Research and Innovations. 2016, 114–119Google Scholar
  105. 105.
    Huang R, Wei X, Lv C, Li X, Zhang S. Prediction model for user’s QoE in imbalanced dataset. In: Proceedings of the 1st International Conference on Computational Intelligence Theory, Systems and Applications. 2015, 41–45Google Scholar
  106. 106.
    Liu R, Huang R, Qian Y, Wei X, Lu P. Improving user’s quality of experience in imbalanced dataset. In: Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference. 2016, 644–649Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ruochen Huang
    • 1
  • Xin Wei
    • 1
    • 2
  • Liang Zhou
    • 1
    • 2
    Email author
  • Chaoping Lv
    • 1
  • Hao Meng
    • 1
  • Jiefeng Jin
    • 1
  1. 1.School of Telecommunication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.National Engineering Research Center of Communications and NetworkingNanjing University of Posts and TelecommunicationsNanjingChina

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