Explainable Predictive Process Monitoring

被引：54

作者：

Galanti, Riccardo ^{[1
,2
]}

Coma-Puig, Bernat ^{[3
]}

de Leoni, Massimiliano ^{[2
]}

Carmona, Josep ^{[3
]}

Navarin, Nicolo ^{[2
]}

机构：

[1] MyInvenio, Reggio Emilia, Italy

[2] Univ Padua, Padua, Italy

[3] Univ Politecn Cataluna, Barcelona, Spain

来源：

2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020) | 2020年

关键词：

D O I：

10.1109/ICPM49681.2020.00012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

引用

页码：1 / 8

页数：8

共 23 条

[1]

Alvarez-Melis David, 2018, On the robustness of interpretability methods

[2]

[Anonymous], 2011, ARXIV14090473

[3] Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes [J].

Breuker, Dominic ;

Delfmann, Patrick ;

Matzner, Martin ;

Becker, Joerg .

BUSINESS PROCESS MANAGEMENT WORKSHOPS( BPM 2014), 2015, 202 :541-553

[4]

de Leoni M., 2020, ARXIV200801807

[5]

Doshi-Velez F., 2017, ML, V1, DOI 10.48550/arXiv.1702.08608

[6] Predictive Monitoring of Business Processes: A Survey [J].

Eduardo Marquez-Chamorro, Alfonso ;

Resinas, Manuel ;

Ruiz-Cortes, Antonio .

IEEE TRANSACTIONS ON SERVICES COMPUTING, 2018, 11 (06) :962-977

[7]

Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]

[8]

Jain S, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P3543

[9]

Lundberg SM, 2017, ADV NEUR IN, V30

[10] Explainable machine-learning predictions for the prevention of hypoxaemia during surgery [J].

Lundberg, Scott M. ;

Nair, Bala ;

Vavilala, Monica S. ;

Horibe, Mayumi ;

Eisses, Michael J. ;

Adams, Trevor ;

Liston, David E. ;

Low, Daniel King-Wai ;

Newman, Shu-Fang ;

Kim, Jerry ;

Lee, Su-In .

NATURE BIOMEDICAL ENGINEERING, 2018, 2 (10) :749-760

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