Explainable Artificial Intelligence for Improved Modeling of Processes
Velioglu R, Göpfert JP, Artelt A, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 313-325.
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Herausgeber*in
Yin, Hujun;
Camacho, David;
Tino, Peter
Einrichtung
Projekt
GRK Data-NInJa: Standortübergreifendes Graduiertenkolleg
ICU4COVID - ‘Cyber-Physical Intensive Care Medical System for Covid-19’
IMPACT-ML: The implications of conversing with intelligent machines in everyday life on people's beliefs about algorithms, their communication behavior and their relationship building
ICU4COVID - ‘Cyber-Physical Intensive Care Medical System for Covid-19’
IMPACT-ML: The implications of conversing with intelligent machines in everyday life on people's beliefs about algorithms, their communication behavior and their relationship building
Abstract / Bemerkung
In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes’ predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.
Erscheinungsjahr
2022
Buchtitel
Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings
Serientitel
Lecture Notes in Computer Science
Band
13756
Seite(n)
313-325
Konferenz
23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2022)
Konferenzort
Manchester, UK
Konferenzdatum
2022-11-24 – 2022-11-26
ISBN
978-3-031-21752-4
eISBN
978-3-031-21753-1
Page URI
https://pub.uni-bielefeld.de/record/2967296
Zitieren
Velioglu R, Göpfert JP, Artelt A, Hammer B. Explainable Artificial Intelligence for Improved Modeling of Processes. In: Yin H, Camacho D, Tino P, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. Vol 13756. Cham: Springer International Publishing; 2022: 313-325.
Velioglu, R., Göpfert, J. P., Artelt, A., & Hammer, B. (2022). Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 313-325). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_31
Velioglu, Riza, Göpfert, Jan Philip, Artelt, André, and Hammer, Barbara. 2022. “Explainable Artificial Intelligence for Improved Modeling of Processes”. In Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, ed. Hujun Yin, David Camacho, and Peter Tino, 13756:313-325. Lecture Notes in Computer Science. Cham: Springer International Publishing.
Velioglu, R., Göpfert, J. P., Artelt, A., and Hammer, B. (2022). “Explainable Artificial Intelligence for Improved Modeling of Processes” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Yin, H., Camacho, D., and Tino, P. eds. Lecture Notes in Computer Science, vol. 13756, (Cham: Springer International Publishing), 313-325.
Velioglu, R., et al., 2022. Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 313-325.
R. Velioglu, et al., “Explainable Artificial Intelligence for Improved Modeling of Processes”, Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, H. Yin, D. Camacho, and P. Tino, eds., Lecture Notes in Computer Science, vol. 13756, Cham: Springer International Publishing, 2022, pp.313-325.
Velioglu, R., Göpfert, J.P., Artelt, A., Hammer, B.: Explainable Artificial Intelligence for Improved Modeling of Processes. In: Yin, H., Camacho, D., and Tino, P. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. 13756, p. 313-325. Springer International Publishing, Cham (2022).
Velioglu, Riza, Göpfert, Jan Philip, Artelt, André, and Hammer, Barbara. “Explainable Artificial Intelligence for Improved Modeling of Processes”. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Ed. Hujun Yin, David Camacho, and Peter Tino. Cham: Springer International Publishing, 2022.Vol. 13756. Lecture Notes in Computer Science. 313-325.