Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 679–682 (2018)
Abstract. Few approaches allow assertive estimates for ticket completion time in incident management. The accuracy level of prediction models depends on how useful the used attributes are. Moreover, to effectively use computational resources, a canonical attribute subset must be used. This paper proposes two automated attribute selection methods to build prediction model. A filter method and two wrapper search techniques were combined with annotated transition systems to automate attribute selectors applied to a real-life incident management process. The results show that the proposed methods surpass human experts' decision making, having wrapper surpassed filter.
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