Abstract
Contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we propose to treat process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; and (4) apply this miner to the real world logs obtained from our industry partner, showing increased output model quality in terms of accuracy and model size.
Original language | English |
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Title of host publication | Business Process Management - 19th International Conference, BPM 2021, Proceedings |
Editors | Artem Polyvyanyy, Moe Thandar Wynn, Amy Van Looy, Manfred Reichert |
Publisher | Springer |
Publication date | 2021 |
Pages | 47-64 |
ISBN (Print) | 9783030854683 |
DOIs | |
Publication status | Published - 2021 |
Event | 19th International Conference on Business Process Management, BPM 2021 - Rome, Italy Duration: 6 Sep 2021 → 10 Sep 2021 |
Conference
Conference | 19th International Conference on Business Process Management, BPM 2021 |
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Country/Territory | Italy |
City | Rome |
Period | 06/09/2021 → 10/09/2021 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12875 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:© 2021, Springer Nature Switzerland AG.
Keywords
- Binary classification
- Labelled event logs
- Negative examples
- Process mining