Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Attribute Selection with Filter and Wrapper: An Application on Incident Management Process

, ,

DOI: http://dx.doi.org/10.15439/2018F126

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 679682 ()

Full text

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.


  1. itSMF, “Global survey on IT service management,” The IT Service Management Forum, 2013, http://www.itil.co.il.
  2. M. Marrone, F. Gacenga, A. Cater-Steel, and L. Kolbe, “IT service management: A cross-national study of ITIL adoption,” Communic. of the Association for Inform. Sys., vol. 34, pp. 49.1–49.30, 2014.
  3. M. de Leoni, W. M. van der Aalst, and M. Dees, “A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs,” Inform. Syst., vol. 56, pp. 235–257, 2016.
  4. W. M. P. van der Aalst, Process Mining – Discovery, Conformance and Enhancement of Business Processes, 2nd ed. Springer, 2016.
  5. W. van der Aalst, M. Schonenberg, and M. Song, “Time prediction based on process mining,” Inform. Syst., vol. 36, no. 2, pp. 450–475, 2011.
  6. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. of Machine Learning Res., vol. 3, pp. 1157–1182, 2003.
  7. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intel., vol. 97, no. 1, pp. 273–324, 1997.
  8. W. M. P. van der Aalst, V. Rubin, H. M. W. Verbeek, B. F. van Dongen, E. Kindler, and C. W. Günther, “Process mining: A two-step approach to balance between underfitting and overfitting,” Software & Systems Modeling, vol. 9, no. 1, 2008.
  9. A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artif. Intel., vol. 97, no. 1-2, pp. 245–271, 1997.
  10. J. S. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: Empirical comparisons,” Int. J. of Forecasting, vol. 8, no. 1, pp. 69 – 80, 1992.
  11. A. de Myttenaere, B. Golden, B. L. Grand, and F. Rossi, “Mean absolute percentage error for regression models,” Neurocomputing, vol. 192, pp. 38–48, 2016.
  12. M. Polato, A. Sperduti, A. Burattin, and M. de Leoni, “Data-aware remaining time prediction of business process instances,” in Proc. of the 2014 Int. Joint Conf. on Neural Netw. IEEE, 2014, pp. 816–823.
  13. A. Rogge-Solti, L. Vana, and J. Mendling, “Time series petri net models – enrichment and prediction,” in Proc. of the 5th Int. Symp. on Data-driven Process Discovery and Analysis (SIMPDA), 2015, pp. 109–123.
  14. M. Hinkka, T. Lehto, K. Heljanko, and A. Jung, “Structural feature selection for event logs,” pp. 20–35, 2017.
  15. J. Evermann, J.-R. Rehse, and P. Fettke, “Predicting process behaviour using deep learning,” Decision Supp. Sys., vol. 100, pp. 129 – 140, 2017.
  16. N. Tax, I. Verenich, M. La Rosa, and M. Dumas, “Predictive business process monitoring with lstm neural networks,” pp. 477–492, 2017.
  17. J. T. E. Richardson, “Eta squared and partial eta squared as measures of effect size in educational research,” Educational Research Review, vol. 6, no. 2, pp. 135–147, 2011.