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http://hdl.handle.net/1942/34547
Title: | Classifying process deviations with weak supervision | Authors: | LAGHMOUCH, Manal JANS, Mieke DEPAIRE, Benoit |
Issue Date: | 2020 | Publisher: | IEEE COMPUTER SOC | Source: | 2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020), IEEE COMPUTER SOC, p. 89 -96 | Abstract: | Although conformance checking is great at detecting process deviations, it still poses challenges that hinders adoption in auditing practice. A major challenge is that in real life a large number of deviating cases is often detected of which only a small amount are true anomalies and thus of real interest to auditors. The number of deviations are often too large to inspect one by one, which explains why auditing requires a sample-based approach. This paper contributes to the research on the practical feasibility of continuous auditing and studies the potential of weak supervision to classify deviations into anomalies and exceptions, allowing auditors to do a full-population analysis of the identified deviations. The Snorkel framework is applied which uses a set of imperfect domain expert rules to classify the set of deviations into anomalies and exceptions. A controlled and artificial experiment has been set up to explore the relation between the performance of this approach and the number and quality of domain expert rules. The results demonstrate the potential of this approach as a limited number of medium to high quality domain expert rules succeeds to classify deviations with acceptable accuracy. | Keywords: | auditing;conformance checking;declare;process deviation;snorkel;weak supervision | Document URI: | http://hdl.handle.net/1942/34547 | ISBN: | 978-1-7281-9832-3 | DOI: | 10.1109/ICPM49681.2020.00023 | ISI #: | WOS:000632751400012 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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Classifying_process_deviations_with_weak_supervision.pdf Restricted Access | Published version | 153.82 kB | Adobe PDF | View/Open Request a copy |
Classifying_PD_WS_v_author.pdf | Peer-reviewed author version | 308.9 kB | Adobe PDF | View/Open |
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