Application of machine learning techniques for automated classification and routing in ITIL library

Abstract

The article analyzes applying the machine learning methods for automated classification and routing in the ITIL library. The ITSM technology and the ITIL library are considered, the definitions to the incident and IT services are given. Further, the vectorization and extraction of keywords in the information written in natural language is carried out, for which lemmatization and the TF-IDF measure will be used. A comparative analysis of the application of machine learning methods, as well as a comparison of the results of automatic classification of text information using gradient boosting and a convolutional neural network is presented. Various parameters of these methods are considered. Gradient boosting showed the best results for the training and test sampling - 95% of correctly classified incidents; in cases with a neural network the result made 91%, a convolutional neural network had 92%. The accuracy of the handwritten classifier is 90%, as some of the incidents do not fall under its terms and remain unclassified. The results of the machine learning methods application for the automated classification of incidents make it possible to route requests for the restoration of the operability of IT services with high accuracy, to reduce the response time and errors associated with the human factor.