In recent years, Automated Machine Learning (AutoML) has become increasingly impor-tant in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.

Vaccaro, L., Sansonetti, G., & Micarelli, A. (2021). An empirical review of automated machine learning. COMPUTERS, 10(1), 1-27 [10.3390/computers10010011].

An empirical review of automated machine learning

Sansonetti G.
;
Micarelli A.
2021

Abstract

In recent years, Automated Machine Learning (AutoML) has become increasingly impor-tant in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.
Vaccaro, L., Sansonetti, G., & Micarelli, A. (2021). An empirical review of automated machine learning. COMPUTERS, 10(1), 1-27 [10.3390/computers10010011].
File in questo prodotto:
File Dimensione Formato  
computers-10-00011.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 7.23 MB
Formato Adobe PDF
7.23 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/385362
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 4
social impact