Skip to main content
SpringerLink
Log in

Active Matrix Completion for Algorithm Selection

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11943))

Abstract

The present work accommodates active matrix completion to generate cheap and informative incomplete algorithm selection datasets. Algorithm selection is being used to detect the best possible algorithm(s) for a given problem (\(\sim \) instance). Although its success has been shown in varying problem domains, the performance of an algorithm selection technique heavily depends on the quality of the existing dataset. One critical and likely to be the most expensive part of an algorithm selection dataset is its performance data. Performance data involves the performance of a group of algorithms on a set of instance of a particular problem. Thus, matrix completion [1] has been studied to be able to perform algorithm selection when the performance data is incomplete. The focus of this study is to come up with a strategy to generate/sample low-cost, incomplete performance data that can lead to effective completion results. For this purpose, a number of matrix completion methods are utilized in the form of active matrix completion. The empirical analysis carried out on a set of algorithm selection datasets revealed significant gains in terms of the computation time, required to produce the relevant performance data.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    http://www.satcompetition.org/.

  2. 2.

    http://aslib.net.

  3. 3.

    https://www.coseal.net/open-algorithm-selection-challenge-2017-oasc/.

  4. 4.

    http://www.netflixprize.com.

  5. 5.

    From fancyimpute 0.1.0: https://pypi.python.org/pypi/fancyimpute.

  6. 6.

    http://aslib.net.

References

  1. Mısır, M., Sebag, M.: ALORS: an algorithm recommender system. Artif. Intell. 244, 291–314 (2017)

    Article  MathSciNet  Google Scholar 

  2. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  3. Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3–45 (2019). MIT Press

    Article  Google Scholar 

  4. Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)

    Article  MathSciNet  Google Scholar 

  5. Bischl, B., et al.: ASlib: a benchmark library for algorithm selection. Artif. Intell. 237, 41–58 (2017)

    Article  MathSciNet  Google Scholar 

  6. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)

    Article  Google Scholar 

  7. Ribeiro, J., Carmona, J., Mısır, M., Sebag, M.: A recommender system for process discovery. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 67–83. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_5

    Chapter  Google Scholar 

  8. Chakraborty, S., Zhou, J., Balasubramanian, V., Panchanathan, S., Davidson, I., Ye, J.: Active matrix completion. In: The 13th IEEE ICDM, pp. 81–90 (2013)

    Google Scholar 

  9. Ruchansky, N., Crovella, M., Terzi, E.: Matrix completion with queries. In: Proceedings of the 21th ACM SIGKDD KDD, pp. 1025–1034 (2015)

    Google Scholar 

  10. Mısır, M.: Data sampling through collaborative filtering for algorithm selection. In: the 16th IEEE CEC, pp. 2494–2501 (2017)

    Google Scholar 

  11. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32(1), 565–606 (2008)

    Article  Google Scholar 

  12. Yun, X., Epstein, S.L.: Learning algorithm portfolios for parallel execution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, pp. 323–338. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34413-8_23

    Chapter  Google Scholar 

  13. Beham, A., Affenzeller, M., Wagner, S.: Instance-based algorithm selection on quadratic assignment problem landscapes. In: ACM GECCO Comp, pp. 1471–1478 (2017)

    Google Scholar 

  14. Vanschoren, J.: Meta-learning: a survey. arXiv preprint arXiv:1810.03548 (2018)

  15. Stephenson, M., Renz, J.: Creating a hyper-agent for solving angry birds levels. In: AAAI AIIDE (2017)

    Google Scholar 

  16. Xu, L., Hutter, F., Shen, J., Hoos, H., Leyton-Brown, K.: SATzilla2012: improved algorithm selection based on cost-sensitive classification models. In: Proceedings of SAT Challenge 2012: Solver and Benchmark Descriptions, pp. 57–58 (2012)

    Google Scholar 

  17. Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: The 23rd IJCAI, pp. 608–614 (2013)

    Google Scholar 

  18. Stern, D., Herbrich, R., Graepel, T., Samulowitz, H., Pulina, L., Tacchella, A.: Collaborative expert portfolio management. In: The 24th AAAI, pp. 179–184 (2010)

    Google Scholar 

  19. Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online Bayesian recommendations. In: The 18th ACM WWW, pp. 111–120 (2009)

    Google Scholar 

  20. Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: The 24th AAAI, pp. 210–216 (2010)

    Google Scholar 

  21. Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC-instance-specific algorithm configuration. In: The 19th ECAI, pp. 751–756 (2010)

    Google Scholar 

  22. Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_35

    Chapter  Google Scholar 

  23. Loreggia, A., Malitsky, Y., Samulowitz, H., Saraswat, V.A.: Deep learning for algorithm portfolios. In: The 13th AAAI, pp. 1280–1286 (2016)

    Google Scholar 

  24. Lindauer, M., Hoos, H.H., Hutter, F., Schaub, T.: AutoFolio: an automatically configured algorithm selector. JAIR 53, 745–778 (2015)

    Article  Google Scholar 

  25. Lindauer, M., van Rijn, J.N., Kotthoff, L.: The algorithm selection competitions 2015 and 2017. arXiv preprint arXiv:1805.01214 (2018)

  26. Kotthoff, L.: ICON challenge on algorithm selection. arXiv preprint arXiv:1511.04326 (2015)

  27. Lindauer, M., van Rijn, J.N., Kotthoff, L.: Open algorithm selection challenge 2017: setup and scenarios. In: OASC 2017, pp. 1–7 (2017)

    Google Scholar 

  28. Gonard, F., Schoenauer, M., Sebag, M.: Algorithm selector and prescheduler in the ICON challenge. In: Talbi, E.-G., Nakib, A. (eds.) Bioinspired Heuristics for Optimization. SCI, vol. 774, pp. 203–219. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-95104-1_13

    Chapter  Google Scholar 

  29. Pillay, N., Qu, R.: Hyper-Heuristics: Theory and Applications. Natural Computing Series. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96514-7

    Book  Google Scholar 

  30. Da Costa, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandits. In: GECCO, pp. 913–920 (2008)

    Google Scholar 

  31. Bennett, J., Lanning, S., et al.: The netflix prize. In: Proceedings of KDD Cup and Workshop, New York, NY, USA, vol. 2007, p. 35 (2007)

    Google Scholar 

  32. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  33. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) The 21st NIPS, pp. 1257–1264 (2007)

    Google Scholar 

  34. Weimer, M., Karatzoglou, A., Le, Q.V., Smola, A.: CofiRank - maximum margin matrix factorization for collaborative ranking. In: The 21st NIPS, pp. 222–230 (2007)

    Google Scholar 

  35. Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: The 4th ACM RecSys, pp. 269–272 (2010)

    Google Scholar 

  36. Candes, E.J., Plan, Y.: Matrix completion with noise. Proc. IEEE 98(6), 925–936 (2010)

    Article  Google Scholar 

  37. Wang, Y.X., Zhang, Y.J.: Nonnegative matrix factorization: a comprehensive review. IEEE TKDE 25(6), 1336–1353 (2013)

    Google Scholar 

  38. Gillis, N., Vavasis, S.A.: Fast and robust recursive algorithms for separable nonnegative matrix factorization. IEEE TPAMI 36(4), 698–714 (2014)

    Article  Google Scholar 

  39. Sutherland, D.J., Póczos, B., Schneider, J.: Active learning and search on low-rank matrices. In: Proceedings of the 19th ACM SIGKDD KDD, pp. 212–220 (2013)

    Google Scholar 

  40. Mazumder, R., Hastie, T., Tibshirani, R.: Spectral regularization algorithms for learning large incomplete matrices. JMLR 11, 2287–2322 (2010)

    MathSciNet  MATH  Google Scholar 

  41. Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)

    Article  Google Scholar 

  42. Azur, M.J., Stuart, E.A., Frangakis, C., Leaf, P.J.: Multiple imputation by chained equations: what is it and how does it work? Int. J. Methods Psych. Res. 20(1), 40–49 (2011)

    Article  Google Scholar 

  43. Gomes, C., Selman, B.: Algorithm portfolio design: theory vs. practice. In: The 13th UAI, pp. 190–197 (1997)

    Google Scholar 

Download references

Acknowledgement

This study was partially supported by an ITC Conference Grant from the COST Action CA15140.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Istinye University, Istanbul, Turkey

    Mustafa Mısır

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Mustafa Mısır

Authors
  1. Mustafa Mısır
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa Mısır .

Editor information

Editors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. Harvard University, Cambridge, MA, USA

    Renato Umeton

  4. Università di Catania, Catania, Catania, Italy

    Giovanni Giuffrida

  5. Almawave, Rome, Roma, Italy

    Vincenzo Sciacca

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mısır, M. (2019). Active Matrix Completion for Algorithm Selection. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37599-7_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37598-0

  • Online ISBN: 978-3-030-37599-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation