Multiclass Classifiers for Processing Archives of Accidents in Manufacturing

The impossibility of total state control in hundreds of thousands enterprises leads to numerous industrial accidents that is a significant social problem. The traditional way for solution of this problem includes careful work of experts with the archives of accidents, which reflect their causes and preventive measures to avoid such cases in the future. Nowadays, the work with these archives is carried out manually due to the lack of tools for processing acts of accidents. In this paper we offer classifiers for these archives, which could facilitate the work of experts and thus improve the quality of their solutions. Our contribution is: 1) comparison and choice of the best procedures for preprocessing data, and 2) choice and adjustment of the best methods of classifications. The program platform is Python. The results of experiments proved to be very promising.