Découverte de sous-groupes de prédictions interprétables pour le triage d'incidents

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},
  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}
}