Journal of the Korea Computer Graphics Society (한국컴퓨터그래픽스학회논문지)

Korea Computer Graphics Society (한국컴퓨터그래픽스학회)
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Visual Analytics Approach for Performance Improvement of predicting youth physical growth model

청소년 신체 성장 예측 모델의 성능 향상을 위한 시각적 분석 방법

  • Published : 2017.09.01
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Abstract

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

예측 시각적 분석 연구는 다양한 대화식 데이터 탐색 기법을 사용하여 예측 결과의 불확실성을 줄이는데 중점을 두었다. 대화식 탐색 기법의 목적은 변수간의 관계를 이해하고 알려지지 않은 변수를 예측하기 위한 적합한 모델을 선택함으로서 의사결정권자의 수준에 따른 예측결과의 품질 차이를 줄이는 것이다. 하지만 청소년 신체 성장 데이터와 같이 전체적인 추세가 알려지지 않은 시계열 데이터를 설명할 수 있는 예측 모델을 만드는 것은 어렵다. 본 논문에서는 불확실한 추세를 가지는 시계열 데이터 단편에서 물리적 성장 값을 예측하기 위한 새로운 예측 방법을 제안한다. 새로운 예측 방법은 특정 시점에서의 데이터 분포를 추정하는 방법으로 실험결과 기존 회귀 모델보다 높은 정확도를 갖는다. 또한 우리는 예측 모델링 과정에서 발생 가능한 불확실성을 최소화 할 수 있는 시각적 분석 방법을 제안한다.

Keywords

References

  1. J. Lu, W. Chen, Y. Ma, J. Ke, Z. Li, F. Zhang, and R. Maciejewski, "Recent progress and trends in predictive visual analytics," Frontiers of Computer Science, pp. 1-16, 2016.
  2. M. El-Assady, W. Jentner, M. Stein, F. Fischer, T. Schreck, and D. Keim, "Predictive visual analytics: Approaches for movie ratings and discussion of open research challenges," in An IEEE VIS 2014 Workshop: Visualization for Predictive Analytics, 2014.
  3. J. Krause, A. Perer, and E. Bertini, "Infuse: interactive feature selection for predictive modeling of high dimensional data," IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1614-1623, 2014. https://doi.org/10.1109/TVCG.2014.2346482
  4. M. Bogl, W. Aigner, P. Filzmoser, T. Lammarsch, S. Miksch, and A. Rind, "Visual analytics for model selection in time series analysis," IEEE transactions on visualization and computer graphics, vol. 19, no. 12, pp. 2237-2246, 2013. https://doi.org/10.1109/TVCG.2013.222
  5. J. Krause, A. Perer, and K. Ng, "Interacting with predictions: Visual inspection of black-box machine learning Figure models," in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.
  6. N. Andrienko, G. Andrienko, and S. Rinzivillo, "Experiences from supporting predictive analytics of vehicle traffic," in Proc. IEEE VIS Workshop Vis. Predictive Anal., 2014.
  7. T. Hollt, A. Magdy, P. Zhan, G. Chen, G. Gopalakrishnan, I. Hoteit, C. D. Hansen, and M. Hadwiger, "Ovis: A framework for visual analysisof ocean forecast ensembles," IEEE transactions on visualization and computer graphics, vol. 20, no. 8, pp. 1114-1126, 2014. https://doi.org/10.1109/TVCG.2014.2307892
  8. A. Malik, R. Maciejewski, S. Towers, S. McCullough, and D. S. Ebert, "Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement," IEEE transactions on visualization and computer graphics, vol. 20, no. 12, pp. 1863-1872, 2014. https://doi.org/10.1109/TVCG.2014.2346926
  9. H. Yeon, S. Kim, and Y. Jang, "Predictive visual analytics of event evolution for user-created context," Journal of Visualization, pp. 1-16, 2016.
  10. T. Muhlbacher and H. Piringer, "A partition-based framework for building and validating regression models," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 1962-1971, 2013. https://doi.org/10.1109/TVCG.2013.125
  11. M. C. Hao, H. Janetzko, S. Mittelstädt, W. Hill, U. Dayal, D. A. Keim, M. Marwah, and R. K. Sharma, "A visual analytics approach for peak-preserving prediction of large seasonal time series," in Computer Graphics Forum. Wiley Online Library, 2011, pp. 691-700.
  12. F. Heimerl, S. Koch, H. Bosch, and T. Ertl, "Visual classifier training for text document retrieval," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pp. 2839-2848, 2012. https://doi.org/10.1109/TVCG.2012.277
  13. S. Koch, M. John, M. Wörner, A. Muller, and T. Ertl, "Varifocalreader in-depth visual analysis of large text - documents," IEEE transactions on visualization and computer graphics, vol. 20, no. 12, pp. 1723-1732, 2014. https://doi.org/10.1109/TVCG.2014.2346677
  14. J. Choo, H. Lee, J. Kihm, and H. Park, "ivisclassifier: An interactive visual analytics system for classification based on supervised dimension reduction," in Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on. IEEE, 2010, pp. 27-34.
  15. J. Choo, C. Lee, C. K. Reddy, and H. Park, "Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization," IEEE transactions on visualization and computer graphics, vol. 19, no. 12, pp. 1992-2001, 2013. https://doi.org/10.1109/TVCG.2013.212
  16. H. Bosch, D. Thom, F. Heimerl, E. Puttmann, S. Koch, R. Kruger, M. Worner, and T. Ertl, "Scatterblogs2: Real-time monitoring of microblog messages through user-guided filtering," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2022-2031, 2013. https://doi.org/10.1109/TVCG.2013.186
  17. J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, and T. Ertl, "Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition," in Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on. IEEE, 2012, pp. 143-152.
  18. G. Sun, Y. Wu, S. Liu, T.-Q. Peng, J. J. Zhu, and R. Liang, "Evoriver: Visual analysis of topic coopetition on social media," IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1753-1762, 2014. https://doi.org/10.1109/TVCG.2014.2346919
  19. C. Bryan, X. Wu, S. Mniszewski, and K.-L. Ma, "Integrating predictive analytics into a spatiotemporal epidemic simulation," in Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. IEEE, 2015, pp. 17-24.
  20. L. Liu, M. Mirzargar, R. Kirby, R. Whitaker, and D. H. House, "Visualizing time-specific hurricane predictions, with uncertainty, from storm path ensembles," Computer Graphics Forum, vol. 34, no. 3, pp. 371-380, 2015.
  21. A. D. Rogol, P. A. Clark, and J. N. Roemmich, "Growth and pubertal development in children and adolescents: effects of diet and physical activity," The American journal of clinical nutrition, vol. 72, no. 2, pp. 521s-528s, 2000. https://doi.org/10.1093/ajcn/72.2.521S
  22. L. M. Gauld, J. Kappers, J. B. Carlin, and C. F. Robertson, "Height prediction from ulna length," Developmental medicine and child neurology, vol. 46, no. 7, pp. 475-480, 2004. https://doi.org/10.1111/j.1469-8749.2004.tb00508.x
  23. M. Onis, "Who child growth standards based on length/height, weight and age," Acta paediatrica, vol. 95, no. S450, pp. 76-85, 2006.
  24. J. S. Armstrong and F. Collopy, "Error measures for generalizing about forecasting methods: Empirical comparisons," International journal of forecasting, vol. 8, no. 1, pp. 69-80, 1992. https://doi.org/10.1016/0169-2070(92)90008-W
  25. M . Bostock, V. Ogievetsky, and J. Heer, "D3 data-driven documents," IEEE transactions on visualization and computer graphics, vol. 17, no. 12, pp. 2301-2309, 2011. https://doi.org/10.1109/TVCG.2011.185
  26. R .C .Team , "R :A language and environment for statistical computing. r foundation for statistical computing, vienna, austria." 2013.
  27. Inselberg, Alfred. "The plane with parallel coordinates." The visual computer 1.2, pp. 69-91, 1985. https://doi.org/10.1007/BF01898350