Advertisement

Visual Guidance to Find the Right Spot in Parameter Space

  • Alexander Brakowski
  • Sebastian Maier
  • Arjan KuijperEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10904)

Abstract

The last few decades brought upon a technological revolution that has been generating data by users with an ever increasing variety of digital devices, resulting in such an incredible volume of data, that we are unable to make any sense of it any more. One solution to decrease the required execution time of these algorithms would be the preprocessing of the data by sampling it before starting the exploration process. That indeed does help, but one issue remains when using the available Machine Learning and Data Mining algorithms: they all have parameters. That is a big problem for most users, because a lot of these parameters require expert knowledge to be able to tune them. Even for expert users a lot of the parameter configurations highly depend on the data. In this work we will present a system that tackles that data exploration process from the angle of parameter space exploration. Here we use the active learning approach and iteratively try to query the user for their opinion of an algorithm execution. For that an end-user only has to express a preference for algorithm results presented to them in form of a visualisations. That way the system is iteratively learning the interest of the end-user, which results in good parameters at the end of the process. A good parametrisation is obviously very subjective here and only reflects the interest of an user. This solution has the nice ancillary property of omitting the requirement of expert knowledge when trying to explore an data set with Data Mining or Machine Learning algorithms. Optimally the end-user does not even know what kind of parameters the algorithms require.

Keywords

Big data Visualization Parameter space Filtering 

References

  1. 1.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Brakowski, A.: Visual guidance to find the right spot in the parameter space. Technical report, TU Darmstadt (2015)Google Scholar
  3. 3.
    Breyer, M., Nazemi, K., Stab, C., Burkhardt, D., Kuijper, A.: A comprehensive reference model for personalized recommender systems. In: Smith, M.J., Salvendy, G. (eds.) Human Interface 2011. LNCS, vol. 6771, pp. 528–537. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21793-7_60CrossRefGoogle Scholar
  4. 4.
    Brochu, E., Cora, V.M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR abs/1012.2599 (2010)Google Scholar
  5. 5.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  6. 6.
    Eric, B., de Freitas, N., Ghosh, A.: Active preference learning with discrete choice data. In: Advances in Neural Information Processing Systems, vol. 20, pp. 409–416. MIT Press, Cambridge (2007)Google Scholar
  7. 7.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification (2010)Google Scholar
  8. 8.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25566-3_40CrossRefGoogle Scholar
  9. 9.
    Kuijper, A.: On detecting all saddle points in 2D images. Pattern Recogn. Lett. 25(15), 1665–1672 (2004)CrossRefGoogle Scholar
  10. 10.
    Kuijper, A.: Using catastrophe theory to derive trees from images. J. Math. Imaging Vis. 23(3), 219–238 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kuijper, A., Florack, L.: The relevance of non-generic events in scale space models. Int. J. Comput. Vis. 57(1), 67–84 (2004)CrossRefGoogle Scholar
  12. 12.
    von Landesberger, T., Bremm, S., Kirschner, M., Wesarg, S., Kuijper, A.: Visual analytics for model-based medical image segmentation: opportunities and challenges. Expert Syst. Appl. 40(12), 4934–4943 (2013)CrossRefGoogle Scholar
  13. 13.
    von Landesberger, T., Fiebig, S., Bremm, S., Kuijper, A., Fellner, D.W.: Interaction taxonomy for tracking of user actions in visual analytics applications. In: Huang, W. (ed.) Handbook of Human Centric Visualization, pp. 653–670. Springer, New York (2014).  https://doi.org/10.1007/978-1-4614-7485-2_26CrossRefGoogle Scholar
  14. 14.
    Marks, J., Andalman, B., Beardsley, P.A., Freeman, W., Gibson, S., Hodgins, J., Kang, T., Mirtich, B., Pfister, H., Ruml, W., Ryall, K., Seims, J., Shieber, S.: Design galleries: a general approach to setting parameters for computer graphics and animation. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 389–400 (1997)Google Scholar
  15. 15.
    Nazemi, K., Stab, C., Kuijper, A.: A reference model for adaptive visualization systems. In: Jacko, J.A. (ed.) HCI 2011. LNCS, vol. 6761, pp. 480–489. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21602-2_52CrossRefGoogle Scholar
  16. 16.
    Osugi, T., Kim, D., Scott, S.: Balancing exploration and exploitation: a new algorithm for active machine learning. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), p. 8 (2005)Google Scholar
  17. 17.
    Pretorius, A.J., Bray, M.A., Carpenter, A.E., Ruddle, R.A.: Visualization of parameter space for image analysis. IEEE Trans. Vis. Comput. Graph. 17(12), 2402–2411 (2011)CrossRefGoogle Scholar
  18. 18.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. ACM, New York (1994)Google Scholar
  19. 19.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  20. 20.
    Settles, B.: Active learning literature survey. Technical report, University of Wisconsin (2010)Google Scholar
  21. 21.
    Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 2, pp. 2951–2959. Curran Associates Inc., New York (2012)Google Scholar
  22. 22.
    Tille, Y.: Sampling Algorithms. Springer, New York (2006).  https://doi.org/10.1007/0-387-34240-0CrossRefzbMATHGoogle Scholar
  23. 23.
    Torsney-Weir, T., Saad, A., Moller, T., Hege, H.C., Weber, B., Verbavatz, J.M., Bergner, S.: Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration. IEEE Trans. Vis. Comput. Graph. 17(12), 1892–1901 (2011)CrossRefGoogle Scholar
  24. 24.
    Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. 11(1), 37–57 (1985)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alexander Brakowski
    • 1
  • Sebastian Maier
    • 2
  • Arjan Kuijper
    • 1
    • 2
    Email author
  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.Fraunhofer IGDDarmstadtGermany

Personalised recommendations