loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Yusuf Arslan 1 ; Bertrand Lebichot 1 ; Kevin Allix 1 ; Lisa Veiber 1 ; Clément Lefebvre 2 ; Andrey Boytsov 2 ; Anne Goujon 2 ; Tegawendé Bissyande 1 and Jacques Klein 1

Affiliations: 1 University of Luxembourg, Luxembourg ; 2 BGL BNP Paribas, Luxembourg

Keyword(s): SHAP Explanations, Shapley Values, Explainable Machine Learning, Clustering, Rule Mining.

Abstract: In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in an ML pipeline is paramount to reduce the workload of experts while increasing customers’ trust. Increasingly, SHAP Explanations are leveraged to facilitate manual analysis. Because they have been shown to be useful to human analysts in the detection of false positives, we postulate that SHAP Explanations may provide a means to automate false-positive reduction. To confirm our intuition, we evaluate clustering and rules detection metrics with ground truth labels to understand the utility of SHAP Explanations to discriminate false positives from true positives. We show that SHAP Explanations are indeed relevant in discriminating samples and are a relevant candidate to automate ML tasks and help to detect and reduce false-positive results.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.139.72.78

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Arslan, Y.; Lebichot, B.; Allix, K.; Veiber, L.; Lefebvre, C.; Boytsov, A.; Goujon, A.; Bissyande, T. and Klein, J. (2022). On the Suitability of SHAP Explanations for Refining Classifications. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 395-402. DOI: 10.5220/0010827700003116

@conference{icaart22,
author={Yusuf Arslan. and Bertrand Lebichot. and Kevin Allix. and Lisa Veiber. and Clément Lefebvre. and Andrey Boytsov. and Anne Goujon. and Tegawendé Bissyande. and Jacques Klein.},
title={On the Suitability of SHAP Explanations for Refining Classifications},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={395-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010827700003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - On the Suitability of SHAP Explanations for Refining Classifications
SN - 978-989-758-547-0
IS - 2184-433X
AU - Arslan, Y.
AU - Lebichot, B.
AU - Allix, K.
AU - Veiber, L.
AU - Lefebvre, C.
AU - Boytsov, A.
AU - Goujon, A.
AU - Bissyande, T.
AU - Klein, J.
PY - 2022
SP - 395
EP - 402
DO - 10.5220/0010827700003116
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic