AutoML: state of the art with a focus on anomaly detection, challenges, and research directions - Equipe Data, Intelligence and Graphs Accéder directement au contenu
Article Dans Une Revue International Journal of Data Science and Analytics Année : 2022

AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

Maroua Bahri
  • Fonction : Auteur
  • PersonId : 1095194
Flavia Salutari
  • Fonction : Auteur
Andrian Putina
  • Fonction : Auteur
Mauro Sozio
  • Fonction : Auteur
  • PersonId : 1027424

Résumé

The last decade has witnessed the explosion of machine learning research studies with the inception of several algorithms proposed and successfully adopted in different application domains. However, the performance of multiple machine learning algorithms is very sensitive to multiple ingredients (e.g., hyper-parameters tuning and data cleaning) where a significant human effort is required to achieve good results. Thus, building well-performing machine learning algorithms requires domain knowledge and highly specialized data scientists. Automated machine learning (autoML) aims to make easier and more accessible the use of machine learning algorithms for researchers with varying levels of expertise. Besides, research effort to date has mainly been devoted to autoML for supervised learning, and only a few research proposals have been provided for the unsupervised learning. In this paper, we present an overview of the autoML field with a particular emphasis on the automated methods and strategies that have been proposed for unsupervised anomaly detection.
Fichier principal
Vignette du fichier
AutoML_state_of_the_art_with_a_focus_on_anomaly_de.pdf (412.27 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03590242 , version 1 (26-02-2022)

Identifiants

Citer

Maroua Bahri, Flavia Salutari, Andrian Putina, Mauro Sozio. AutoML: state of the art with a focus on anomaly detection, challenges, and research directions. International Journal of Data Science and Analytics, 2022, ⟨10.1007/s41060-022-00309-0⟩. ⟨hal-03590242⟩
205 Consultations
2995 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More