Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers

TL;DR

This paper explores the use of ensemble machine learning methods to improve the automatic classification of unstructured IT service desk tickets, aiming to enhance accuracy and efficiency.

Contribution

It introduces the application of Bagging, Boosting, and Voting ensemble techniques to IT ticket classification, demonstrating improved performance over individual classifiers.

Findings

Ensemble classifiers outperform individual models in accuracy.

Boosting achieved the highest classification performance.

Automated classification reduces resolution time and improves customer satisfaction.

Abstract

Manual classification of IT service desk tickets may result in routing of the tickets to the wrong resolution group. Incorrect assignment of IT service desk tickets leads to reassignment of tickets, unnecessary resource utilization and delays the resolution time. Traditional machine learning algorithms can be used to automatically classify the IT service desk tickets. Service desk ticket classifier models can be trained by mining the historical unstructured ticket description and the corresponding label. The model can then be used to classify the new service desk ticket based on the ticket description. The performance of the traditional classifier systems can be further improved by using various ensemble of classification techniques. This paper brings out the three most popular ensemble methods ie, Bagging, Boosting and Voting ensemble for combining the predictions from different models to further improve the accuracy of the ticket classifier system. The performance of the ensemble classifier system is checked against the individual base classifiers using various performance metrics. Ensemble of classifiers performed well in comparison with the corresponding base classifiers. The advantages of building such an automated ticket classifier systems are simplified user interface, faster resolution time, improved productivity, customer satisfaction and growth in business. The real world service desk ticket data from a large enterprise IT infrastructure is used for our research purpose.