Skip to main content
SpringerLink
Log in
Book cover

International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 34–49Cite as

Automatic Configuration of Kernel-Based Clustering: An Optimization Approach

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10556))

Abstract

This paper generalizes a method originally developed by the authors to perform data driven localization of leakages in urban Water Distribution Networks. The method is based on clustering to perform exploratory analysis and a pool of Support Vector Machines to process on line sensors readings. The performance depends on certain hyperparameters which have been considered as decision variables in a sequential model based optimization process. The objective function is related to clustering performance, computed through an external validity index defined according to the leakage localization goal. Thus, as usual in hyperparameters tuning of machine learning algorithms, the objective function is black box. In this paper it is shown how a Bayesian framework offers not only a good performance but also the flexibility to consider in the optimization loop also the automatic configuration of the algorithm. Both Gaussian Processes and Random Forests have been considered to fit the surrogate model of the objective function, while results from a simple grid search have been considered as baseline.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Xia, L., Xiao-dong, W., Xin-hua, Z., Guo-jin, L.: Bayesian theorem based on-line leakage detection and localization of municipal water supply network. Water Wastewater Eng. 12 (2006)

    Google Scholar 

  2. Sivapragasam, C., Maheswaran, R., Venkatesh, V.: ANN-based model for aiding leak detection in water distribution networks. Asian J. Water Environ. Pollut. 5(3), 111–114 (2007)

    Google Scholar 

  3. Xia, L., Guo-jin, L.: Leak detection of municipal water supply network based on the cluster-analysis and fuzzy pattern recognition. In: 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE), vol. 1(5), pp. 7–9 (2010)

    Google Scholar 

  4. Lijuan, W., Hongwei, Z., Hui, J.: A leak detection method based on EPANET and genetic algorithm in water distribution systems. In: Wu, Y. (ed.) Software Engineering and Knowledge Engineering: Theory and Practice. AISC, vol. 114, pp. 459–465. Springer, Heidelberg (2012). doi:10.1007/978-3-642-03718-4_57

    Chapter  Google Scholar 

  5. Nasir, A., Soong, B.H., Ramachandran, S.: Framework of WSN based human centric cyber physical in-pipe water monitoring system. In: 11th International Conference on Control, Automation, Robotics and Vision, pp. 1257–1261 (2010)

    Google Scholar 

  6. Soldevila, A., Fernandez-Canti, R.M., Blesa, J., Tornil-Sin, S., Puig, V.: Leak localization in water distribution networks using Bayesian classifiers. J. Process Control 55, 1–9 (2017)

    Article  Google Scholar 

  7. Franzin, A., Cáceres, L.P., Stützle, T.: Effect of Transformations of Numerical Parameters in Automatic Algorithm Configuration, IRIDIA Technical Report 2017-006 (2017)

    Google Scholar 

  8. Bagnall, A., Cawley, G.C.: On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms. arXiv:1703.06777v1 [cs.LG] (2017)

  9. Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms. Springer, US (2000)

    Book  MATH  Google Scholar 

  10. Zhigljavsky, A., Žilinskas, A.: Stochastic Global Optimization. Springer, US (2008)

    MATH  Google Scholar 

  11. Locatelli, M., Schoen, F.: Global Optimization - Theory, Algorithms and Applications. MOS-SIAM Series on Optimization. Society for Industrial & Applied Mathematics, Philadelphia (2013)

    Book  MATH  Google Scholar 

  12. Sergeyev, Y.D., Kvasov, D.E.: Global search based on efficient diagonal partitions and a set of Lipschitz constants. SIAM J. Optim. 16(3), 910–937 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  13. Sergeyev, Y.D., Kvasov, D.E.: A deterministic global optimization using smooth diagonal auxiliary functions. Commun. Nonlinear Sci. Numer. Simul. 21(1), 99–111 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  14. Barkalov, K., Polovinkin, A., Meyerov, I., Sidorov, S., Zolotykh, N.: SVM regression parameters optimization using parallel global search algorithm. In: Malyshkin, V. (ed.) PaCT 2013. LNCS, vol. 7979, pp. 154–166. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39958-9_14

    Chapter  Google Scholar 

  15. Gillard, J.W., Kvasov, D.E.: Lipschitz optimization methods for fitting a sum of damped sinusoids to a series of observations. Stat. Interface 10, 59–70 (2017)

    Article  MathSciNet  Google Scholar 

  16. Zabinsky, Z.B.: Stochastic Adaptive Search for Global Optimization, vol. 72. Springer, New York (2013)

    MATH  Google Scholar 

  17. Steponavičė, I., Shirazi-Manesh, M., Hyndman, R.J., Smith-Miles, K., Villanova, L.: On sampling methods for costly multiobjective black-box optimization. In: Pardalos, P.M., Zhigljavsky, A., Zilinskas, J. (eds.) Advances in Stochastic and Deterministic Global Optimization. SOIA, vol. 107, pp. 273–296. Springer, Cham (2016). doi:10.1007/978-3-319-29975-4_15

    Chapter  Google Scholar 

  18. Csendes, T., Pál, L., Sendin, J.O.H., Banga, J.R.: The GLOBAL optimization method revisited. Optim. Lett. 2(4), 445–454 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  19. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. arXiv preprint arXiv:1603.06560 (2016)

  20. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962–2970 (2015)

    Google Scholar 

  21. Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: A review of bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)

    Article  Google Scholar 

  22. Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951–2959 (2012)

    Google Scholar 

  23. 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. arXiv preprint arXiv:1012.2599 (2010)

  24. Mockus, J.: On Bayesian methods of optimization. In: Dixon, L.C.W., Szegö, G.P. (eds.) Towards Global Optimization. North-Holland, Amsterdam (1975)

    Google Scholar 

  25. Stützle, T.: Automated algorithm configuration: advances and prospects. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds.) Intelligent Distributed Computing VIII. SCI, vol. 570, p. 5. Springer, Cham (2015). doi:10.1007/978-3-319-10422-5_2

    Google Scholar 

  26. Mala-Jetmarova, H., Sultanova, N., Savic, D.: Lost in optimization of water distribution systems? a literature review of system operations. Environ. Modell. Softw. 93, 209–254 (2017)

    Article  Google Scholar 

  27. 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). doi:10.1007/978-3-642-25566-3_40

    Chapter  Google Scholar 

  28. Candelieri, A., Soldi, D., Archetti, F.: Cost-effective sensors placement and leak localization - The Neptun pilot of the ICeWater project. J. Water Supply: Res. Technol. AQUA 64(5), 567–582 (2015)

    Article  Google Scholar 

  29. Candelieri, A., Soldi, D., Conti, D., Archetti, F.: Analytical leakages localization in water distribution networks through spectral clustering and support vector machines. The icewater approach. Procedia Eng. 89, 1080–1088 (2014)

    Article  Google Scholar 

  30. Candelieri, A., Archetti, F., Messina, E.: Improving leakage management in urban water distribution networks through data analytics and hydraulic simulation. WIT Trans. Ecol. Environ. 171, 107–117 (2013)

    Article  Google Scholar 

  31. Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 551–556 (2004)

    Google Scholar 

  32. Žilinskas, A.: On similarities between two models of global optimization: statistical models and radial basis functions. J. Global Optim. 48(1), 173–182 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  33. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)

    Article  Google Scholar 

  34. Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)

    Article  Google Scholar 

  35. Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  36. Horn, D., Wagner, T., Biermann, D., Weihs, C., Bischl, B.: Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9018, pp. 64–78. Springer, Cham (2015). doi:10.1007/978-3-319-15934-8_5

    Google Scholar 

  37. Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging is well-suited to parallelize optimization. In: Tenne, Y., Goh, C.K. (eds.) Computational Intelligence in Expensive Optimization Problems. ALO, vol. 2, pp. 131–162. Springer, Heidelberg (2010). doi:10.1007/978-3-642-10701-6_6

    Chapter  Google Scholar 

  38. Bischl, B., Wessing, S., Bauer, N., Friedrichs, K., Weihs, C.: MOI-MBO: multiobjective infill for parallel model-based optimization. In: Pardalos, Panos M., Resende, M.G.C., Vogiatzis, C., Walteros, J.L. (eds.) LION 2014. LNCS, vol. 8426, pp. 173–186. Springer, Cham (2014). doi:10.1007/978-3-319-09584-4_17

    Google Scholar 

  39. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyperparameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)

    Google Scholar 

  40. Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., Lang, M.: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373 (2017)

  41. Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of ACM SIGKDD, pp. 847–855 (2013)

    Google Scholar 

  42. Lang, M., Kotthaus, H., Marwedel, P., Weihs, C., Rahnenführer, J., Bischl, B.: Automatic model selection for high-dimensional survival analysis. J. Stat. Comput. Simul. 85(1), 62–76 (2015)

    Article  MathSciNet  Google Scholar 

  43. Horn, D., Bischl, B.: Multi-objective parameter configuration of machine learning algorithms using model-based optimization. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016)

    Google Scholar 

  44. Richter, J., Kotthaus, H., Bischl, B., Marwedel, P., Rahnenführer, J., Lang, M.: Faster model-based optimization through resource-aware scheduling strategies. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 267–273. Springer, Cham (2016). doi:10.1007/978-3-319-50349-3_22

    Chapter  Google Scholar 

  45. Kvasov, D.E., Sergeyev, Y.D.: Deterministic approaches for solving practical black-box global optimization problems. Adv. Eng. Softw. 80, 58–66 (2015)

    Article  Google Scholar 

  46. Wang, Z., Zoghi, M., Hutter, F., Matheson, D., De Freitas, N.: Bayesian optimization in high dimensions via random embeddings. In: AAAI Press/International Joint Conferences on Artificial Intelligence (2013)

    Google Scholar 

  47. Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. arXiv:1605.07079 (2017)

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica Sistemistica e Comunicazione, DISCo, Università di Milano Bicocca, 20126, Milan, Italy

    Antonio Candelieri, Ilaria Giordani & Francesco Archetti

  2. Consorzio Milano Ricerche, via R. Cozzi 53, 20125, Milan, Italy

    Francesco Archetti

Authors
  1. Antonio Candelieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilaria Giordani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Archetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonio Candelieri .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Candelieri, A., Giordani, I., Archetti, F. (2017). Automatic Configuration of Kernel-Based Clustering: An Optimization Approach. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69404-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation