Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers
This addresses the issue of inefficient ticket routing in IT service desks for enterprises, but it is incremental as it applies existing ensemble techniques to a specific domain.
The paper tackled the problem of manually classifying IT service desk tickets, which can lead to incorrect routing and delays, by using ensemble methods like Bagging, Boosting, and Voting to improve classification accuracy, showing that ensemble classifiers performed better than individual base classifiers.
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.