  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.
