Advertisement

Automated Decision Modeling with DMN and BPMN: A Model Ensemble Approach

  • Srđan Daniel SimićEmail author
  • Nikola Tanković
  • Darko Etinger
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

Abstract

Plethora of available heterogeneous transactional data and recent advancements in machine learning are the key forces that enable the development of complex algorithms that can reach human-level performance on an increasing number of tasks. Given the non-linear structure composed of many layers of computation, these highly accurate models are usually applied in a black-box manner: without a deeper understanding of their inner mechanisms. This hinders the transparency of the decision-making process and can often incorporate hidden decision biases which are potentially present in the data. We propose a framework for generating decision-making models conforming to Decision Model & Notation standard based on complexity-reducing techniques. An ensemble of decision-tree classifiers in a layered architecture is proposed to control the bias-variance trade-off. We have evaluated the performance of the proposed method on several publicly available data-sets tightly related to socially sensitive decision-making.

Keywords

Machine learning Automated decision making White-box models 

References

  1. 1.
    Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J.W., Christakis, N.A., Couzin, I.D., Jackson, M.O., Jennings, N.R., Kamar, E., Kloumann, I.M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D.C., Pentland, A., Roberts, M.E., Shariff, A., Tenenbaum, J.B., Wellman, M.: Machine behaviour. Nature 568(7753), 477–486 (2019)CrossRefGoogle Scholar
  2. 2.
    Decision Modelling Notation (DMN) 1.2 Specification, Version 1.2 (2019)Google Scholar
  3. 3.
    Business Process Model and Notation (BPMN) 2.2 Specification, Version 2.2 (2014)Google Scholar
  4. 4.
    Tanković, N., Vukotić, D., Žagar, M.: Rethinking model driven development: analysis and opportunities. In: Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces, pp. 505–510. IEEE (2012)Google Scholar
  5. 5.
    Etinger, D., Simić, S.D., Buljubašić, L.: Automated decision-making with DMN: from decision trees to decision tables. In: Proceedings of the 42nd Inter- national Convention on Information and Communication Technology, Electronics and Microelectronics. Croatian Society for Information and Communication Technology, Electronics and Microelectronics (2019)Google Scholar
  6. 6.
    Dressel, J., Farid, H.: The accuracy fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)CrossRefGoogle Scholar
  7. 7.
    Sinur, J., Geneva, R., Debevoise, T., Taylor, J.: The MicroGuide to Process and Decision Modeling in BPMN/DMN: Building More Effective Processes by Integrating Process Modeling with Decision Modeling. CreateSpace Independent Publishing Platform, October 2014Google Scholar
  8. 8.
    Ayyadevara, K.V.: Gradient boosting machine. In: Proceedings of the Machine Learning Algorithms, pp. 117–134. Apress (2018)Google Scholar
  9. 9.
    Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: Proceedings of the 27th European Conference on Advances in Information Retrieval Research, ECIR 2005, pp. 345– 359. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Srđan Daniel Simić
    • 1
    Email author
  • Nikola Tanković
    • 1
  • Darko Etinger
    • 1
  1. 1.Faculty of InformaticsJuraj Dobrila University of PulaPulaCroatia

Personalised recommendations