Difference between revisions of "Publications/remil.22.egc"
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description that characterises each subgroup<nowiki>}</nowiki>, |
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Latest revision as of 19:08, 7 April 2023
- Authors
- Youcef Remil, Anes Bendimerad, Marc Plantevit, Céline Robardet, Mehdi Kaytoue
- Where
- Extraction et Gestion des Connaissances, EGC 2022Blois, France, 24 au 28 janvier 2022
- Type
- inproceedings
- Keywords
- IA
- Date
- 2022-01-24
Abstract
The need for predictive maintenance comes with an increasing number of incidents, where it is imperative to quickly decide which service to contact for corrective actions. Several predictive models have been designed to automate this process, but the efficient models are opaque (say, black boxes). Many approaches have been proposed to locally explain each prediction of such models. Howeverproviding an explanation for every result is not conceivable when it comes to a large number of daily predictions to analyze. In this article we propose a method based on Subgroup Discovery in order to (1) group together objects that share similar explanations and (2) provide a description that characterises each subgroup
Bibtex (lrde.bib)
@InProceedings{ remil.22.egc,
author = {Youcef Remil and Anes Bendimerad and Marc Plantevit and
C{\'{e}}line Robardet and Mehdi Kaytoue},
title = {D{\'{e}}couverte de sous-groupes de pr{\'{e}}dictions
interpr{\'{e}}tables pour le triage d'incidents},
booktitle = {Extraction et Gestion des Connaissances, {EGC} 2022,
Blois, France, 24 au 28 janvier 2022},
pages = {411--418},
year = {2022},
month = jan,
url = {http://editions-rnti.fr/?inprocid=1002754},
abstract = {The need for predictive maintenance comes with an
increasing number of incidents, where it is imperative to
quickly decide which service to contact for corrective
actions. Several predictive models have been designed to
automate this process, but the efficient models are opaque
(say, black boxes). Many approaches have been proposed to
locally explain each prediction of such models. However,
providing an explanation for every result is not
conceivable when it comes to a large number of daily
predictions to analyze. In this article we propose a method
based on Subgroup Discovery in order to (1) group together
objects that share similar explanations and (2) provide a
description that characterises each subgroup},
note = {In French},
category = {national},
nodoi = {}
}