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.
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Acknowledgements
The work of A.V. Savchenko is supported by Russian Federation President grant no. MD-306.2017.9 and Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics.
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Milov, V.R., Savchenko, A.V. (2017). Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_31
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DOI: https://doi.org/10.1007/978-3-319-52920-2_31
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