Multi-view feature selection for labeling noisy ticket data.

Service providers are facing an increasingly intense competition and growing industry requirements, which dictates efficient and cost-effective service delivery. This is largely achieved by maximizing automation of IT maintenance procedures. Automation it largely depend on classification of the tickets. The automation of ticket classification requires labeled data, which is usually triaged by manually generated rules on word features. In this paper, we propose an unsupervised approach for facilitation of rule generation for labeling tickets created by event management. We first identify and remove noisy tickets with generic resolutions, and then use sparse classic canonical analysis (CCA) for feature selection to enable an efficient rule generation. Furthermore we discuss results of an extensive empirical study of ticket data that was conducted to validate the effectiveness and efficiency of our method.