Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/34547
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | LAGHMOUCH, Manal | - |
dc.contributor.author | JANS, Mieke | - |
dc.contributor.author | DEPAIRE, Benoit | - |
dc.date.accessioned | 2021-07-26T11:46:43Z | - |
dc.date.available | 2021-07-26T11:46:43Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2021-07-12T09:43:12Z | - |
dc.identifier.citation | 2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020), IEEE COMPUTER SOC, p. 89 -96 | - |
dc.identifier.isbn | 978-1-7281-9832-3 | - |
dc.identifier.uri | http://hdl.handle.net/1942/34547 | - |
dc.description.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. | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.subject.other | auditing | - |
dc.subject.other | conformance checking | - |
dc.subject.other | declare | - |
dc.subject.other | process deviation | - |
dc.subject.other | snorkel | - |
dc.subject.other | weak supervision | - |
dc.title | Classifying process deviations with weak supervision | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.conferencedate | OCT 04-09, 2020 | - |
local.bibliographicCitation.conferencename | 2nd International Conference on Process Mining (ICPM) | - |
local.bibliographicCitation.conferenceplace | ELECTR NETWORK | - |
dc.identifier.epage | 96 | - |
dc.identifier.spage | 89 | - |
local.bibliographicCitation.jcat | C1 | - |
local.publisher.place | 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
dc.identifier.doi | 10.1109/ICPM49681.2020.00023 | - |
dc.identifier.isi | WOS:000632751400012 | - |
local.provider.type | wosris | - |
local.bibliographicCitation.btitle | 2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020) | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2022 | - |
item.contributor | LAGHMOUCH, Manal | - |
item.contributor | JANS, Mieke | - |
item.contributor | DEPAIRE, Benoit | - |
item.accessRights | Open Access | - |
item.fullcitation | LAGHMOUCH, Manal; JANS, Mieke & DEPAIRE, Benoit (2020) Classifying process deviations with weak supervision. In: 2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020), IEEE COMPUTER SOC, p. 89 -96. | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
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 |
Page view(s)
54
checked on Sep 7, 2022
Download(s)
90
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.