Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)

Abstract

In this paper we examine the task of maintenance decision support in classification of the dangerous situations discovered by the monitoring system. This task is reduced to the contextual multi-armed bandit problem. We highlight the small sample size problem appeared in this task due to the rather rare failures. The novel algorithm based on the nearest neighbor search is proposed. An experimental study is provided for several synthetic datasets with the situations described by either simple features or grayscale images. It is shown, that our algorithm outperforms the well-known contextual multi-armed methods with the Upper Confidence Bound and softmax stochastic search strategies.

Keywords

Maintenance decision support systems Classification Small sample size problem Nearest neighbor Contextual multi-armed bandit 

References

  1. 1.
    Milov, V.R., Suslov, B.A., Kryukov, O.V.: Intellectual management decision support in gas industry. Autom. Remote Control 72(5), 1095–1101 (2011)CrossRefGoogle Scholar
  2. 2.
    Huynh, K.T., Barros, A., Berenguer, C.: Maintenance decision-making for systems operating under indirect condition monitoring: value of online information and impact of measurement uncertainty. IEEE Trans. Reliab. 61(2), 410–425 (2012)CrossRefGoogle Scholar
  3. 3.
    Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4(2), 107–194 (2011)CrossRefMATHGoogle Scholar
  4. 4.
    Savchenko, A.V., Belova, N.S.: Statistical testing of segment homogeneity in classification of piecewise–regular objects. Int. J. Appl. Math. Comput. Sci. 25(4), 915–925 (2015)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    May, B.C., Korda, N., Lee, A., Leslie, D.S.: Optimistic Bayesian sampling in contextual-bandit problems. J. Mach. Learn. Res. 13(1), 2069–2106 (2012)MathSciNetMATHGoogle Scholar
  6. 6.
    Vermorel, J., Mohri, M.: Multi-armed bandit algorithms and empirical evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 437–448. Springer, Heidelberg (2005). doi:10.1007/11564096_42 CrossRefGoogle Scholar
  7. 7.
    Li, Q., Racine, J.S.: Nonparametric Econometrics: Theory and Practice. Princeton University Press, Princeton (2007)MATHGoogle Scholar
  8. 8.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)Google Scholar
  9. 9.
    Chernousov, V.O., Savchenko, A.V.: A fast mathematical morphological algorithm of video-based moving forklift truck detection in noisy environment. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorskiy, Rostislav, E. (eds.) AIST 2014. CCIS, vol. 436, pp. 57–65. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12580-0_5 Google Scholar
  10. 10.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 4th edn. Cengage Learning, Stamford (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Nizhny Novgorod State Technical University n.a. R.E. AlekseevNizhny NovgorodRussia
  2. 2.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia

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