Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems
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.
KeywordsMaintenance decision support systems Classification Small sample size problem Nearest neighbor Contextual multi-armed bandit
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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