TY - GEN
T1 - Towards a Business-Oriented Approach to Visualization-Supported Interpretability of Prediction Results in Process Mining
AU - Maita, Ana Rocío Cárdenas
AU - Fantinato, Marcelo
AU - Peres, Sarajane Marques
AU - Maggi, Fabrizio Maria
N1 - Publisher Copyright:
Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
PY - 2023
Y1 - 2023
N2 - The majority of the state-of-the-art predictive process monitoring approaches are based on machine learning techniques. However, many machine learning techniques do not inherently provide explanations to business process analysts to interpret the results of the predictions provided about the outcome of a process case and to understand the rationale behind such predictions. In this paper, we introduce a business-oriented approach to visually support the interpretability of the results in predictive process monitoring. We take as input the results produced by the SP-LIME interpreter and we project them onto a process model. The resulting enriched model shows which features contribute to what degree to the predicted result. We exemplify the proposed approach by visually interpreting the results of a classifier to predict the output of a claim management process, whose claims can be accepted or rejected.
AB - The majority of the state-of-the-art predictive process monitoring approaches are based on machine learning techniques. However, many machine learning techniques do not inherently provide explanations to business process analysts to interpret the results of the predictions provided about the outcome of a process case and to understand the rationale behind such predictions. In this paper, we introduce a business-oriented approach to visually support the interpretability of the results in predictive process monitoring. We take as input the results produced by the SP-LIME interpreter and we project them onto a process model. The resulting enriched model shows which features contribute to what degree to the predicted result. We exemplify the proposed approach by visually interpreting the results of a classifier to predict the output of a claim management process, whose claims can be accepted or rejected.
KW - Event Logs
KW - Explainable Machine Learning
KW - Interpretable Machine Learning
KW - Predictive Process Mining
KW - Process Mining
KW - XAI
UR - https://www.scopus.com/pages/publications/85160686697
U2 - 10.5220/0011976000003467
DO - 10.5220/0011976000003467
M3 - Conference contribution
AN - SCOPUS:85160686697
T3 - International Conference on Enterprise Information Systems, ICEIS - Proceedings
SP - 395
EP - 406
BT - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1, ICEIS 2023
A2 - Filipe, Joaquim
A2 - Smialek, Michal
A2 - Brodsky, Alexander
A2 - Hammoudi, Slimane
PB - Science and Technology Publications, Lda
T2 - 25th International Conference on Enterprise Information Systems, ICEIS 2023
Y2 - 24 April 2023 through 26 April 2023
ER -