A Machine Learning-Based Predictive Model for the Management of Incidents in Small and Medium-Sized Enterprises in Peru
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Issue Date
2024-03-22
Metadata
Show full item recordPublisher
Association for Computing MachineryJournal
ACM International Conference Proceeding SeriesDOI
10.1145/3654823.3654913Abstract
In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics provided by the scikit-learn library. Additionally, the entropy loss approaches a value of 0, suggesting a precise ability of the model to predict real values. Additionally, an average accuracy level of 93.74% was achieved.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engae974a485f413a2113503eed53cd6c53
10.1145/3654823.3654913
Scopus Count
Collections