Geodesic
Application of Machine Learning to Incident Ranking at Moscow Railway
Informacionnye tehnologii i vyčislitelnye sistemy, no. 2 (2017), pp. 43-53

Voir la notice de l'article provenant de la source Math-Net.Ru

Moscow Railway, a large railway network including 8800 kilometers of track and 549 stations, is equipped with tens of thousands of devices for automatic registration of system failures. Alerts produced by these devices are processed by operators of the Infrastructure Management Center. The alert flow is very intense and creates a significant stress on the operators while about 97
Keywords: railroad monitoring, incident ranking, machine learning, feature engineering, ensemble of decision trees, XGBoost. 
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     author = {P. Y. Boyko and E. M. Bikov and E. I. Sokolov and D. A. Yarotsky},
     title = {Application of {Machine} {Learning} to {Incident} {Ranking} at {Moscow} {Railway}},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
     pages = {43--53},
     publisher = {mathdoc},
     number = {2},
     year = {2017},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ITVS_2017_2_a3/}
}
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P. Y. Boyko; E. M. Bikov; E. I. Sokolov; D. A. Yarotsky. Application of Machine Learning to Incident Ranking at Moscow Railway. Informacionnye tehnologii i vyčislitelnye sistemy, no. 2 (2017), pp. 43-53. http://geodesic.mathdoc.fr/item/ITVS_2017_2_a3/