Standard
Weighing the Pros and Cons : Process Discovery with Negative Examples. / Slaats, Tijs; Debois, Søren; Back, Christoffer Olling.
Business Process Management - 19th International Conference, BPM 2021, Proceedings. red. / Artem Polyvyanyy; Moe Thandar Wynn; Amy Van Looy; Manfred Reichert. Springer, 2021. s. 47-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12875 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Slaats, T, Debois, S & Back, CO 2021,
Weighing the Pros and Cons: Process Discovery with Negative Examples. i A Polyvyanyy, MT Wynn, A Van Looy & M Reichert (red),
Business Process Management - 19th International Conference, BPM 2021, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 12875 LNCS, s. 47-64, 19th International Conference on Business Process Management, BPM 2021, Rome, Italien,
06/09/2021.
https://doi.org/10.1007/978-3-030-85469-0_6
APA
Slaats, T., Debois, S., & Back, C. O. (2021).
Weighing the Pros and Cons: Process Discovery with Negative Examples. I A. Polyvyanyy, M. T. Wynn, A. Van Looy, & M. Reichert (red.),
Business Process Management - 19th International Conference, BPM 2021, Proceedings (s. 47-64). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 12875 LNCS
https://doi.org/10.1007/978-3-030-85469-0_6
Vancouver
Slaats T, Debois S, Back CO.
Weighing the Pros and Cons: Process Discovery with Negative Examples. I Polyvyanyy A, Wynn MT, Van Looy A, Reichert M, red., Business Process Management - 19th International Conference, BPM 2021, Proceedings. Springer. 2021. s. 47-64. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12875 LNCS).
https://doi.org/10.1007/978-3-030-85469-0_6
Author
Slaats, Tijs ; Debois, Søren ; Back, Christoffer Olling. / Weighing the Pros and Cons : Process Discovery with Negative Examples. Business Process Management - 19th International Conference, BPM 2021, Proceedings. red. / Artem Polyvyanyy ; Moe Thandar Wynn ; Amy Van Looy ; Manfred Reichert. Springer, 2021. s. 47-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12875 LNCS).
Bibtex
@inproceedings{3b5c696a99504dca970c20b46ce16551,
title = "Weighing the Pros and Cons: Process Discovery with Negative Examples",
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.",
keywords = "Binary classification, Labelled event logs, Negative examples, Process mining",
author = "Tijs Slaats and S{\o}ren Debois and Back, {Christoffer Olling}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 19th International Conference on Business Process Management, BPM 2021 ; Conference date: 06-09-2021 Through 10-09-2021",
year = "2021",
doi = "10.1007/978-3-030-85469-0_6",
language = "English",
isbn = "9783030854683",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "47--64",
editor = "Artem Polyvyanyy and Wynn, {Moe Thandar} and {Van Looy}, Amy and Manfred Reichert",
booktitle = "Business Process Management - 19th International Conference, BPM 2021, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Weighing the Pros and Cons
T2 - 19th International Conference on Business Process Management, BPM 2021
AU - Slaats, Tijs
AU - Debois, Søren
AU - Back, Christoffer Olling
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Binary classification
KW - Labelled event logs
KW - Negative examples
KW - Process mining
U2 - 10.1007/978-3-030-85469-0_6
DO - 10.1007/978-3-030-85469-0_6
M3 - Article in proceedings
AN - SCOPUS:85115196600
SN - 9783030854683
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 64
BT - Business Process Management - 19th International Conference, BPM 2021, Proceedings
A2 - Polyvyanyy, Artem
A2 - Wynn, Moe Thandar
A2 - Van Looy, Amy
A2 - Reichert, Manfred
PB - Springer
Y2 - 6 September 2021 through 10 September 2021
ER -