Weighing the Pros and Cons

Weighing the Pros and Cons: Process Discovery with Negative Examples

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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. ed. / Artem Polyvyanyy; Moe Thandar Wynn; Amy Van Looy; Manfred Reichert. Springer, 2021. p. 47-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12875 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Slaats, T, Debois, S & Back, CO 2021, Weighing the Pros and Cons: Process Discovery with Negative Examples. in A Polyvyanyy, MT Wynn, A Van Looy & M Reichert (eds), 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), vol. 12875 LNCS, pp. 47-64, 19th International Conference on Business Process Management, BPM 2021, Rome, Italy, 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. In A. Polyvyanyy, M. T. Wynn, A. Van Looy, & M. Reichert (Eds.), Business Process Management - 19th International Conference, BPM 2021, Proceedings (pp. 47-64). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 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. In Polyvyanyy A, Wynn MT, Van Looy A, Reichert M, editors, Business Process Management - 19th International Conference, BPM 2021, Proceedings. Springer. 2021. p. 47-64. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 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. editor / Artem Polyvyanyy ; Moe Thandar Wynn ; Amy Van Looy ; Manfred Reichert. Springer, 2021. pp. 47-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 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

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

UR - http://www.scopus.com/inward/record.url?scp=85115196600&partnerID=8YFLogxK

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 -

ID: 282680828