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An Empirical Review of Automated Machine Learning

Department of Engineering, Roma Tre University, 00146 Rome, Italy
Author to whom correspondence should be addressed.
Received: 2 November 2020 / Revised: 6 January 2021 / Accepted: 8 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
In recent years, Automated Machine Learning (AutoML) has become increasingly important 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. View Full-Text
Keywords: automated machine learning; meta learning; neural architecture search; reinforcement learning automated machine learning; meta learning; neural architecture search; reinforcement learning
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MDPI and ACS Style

Vaccaro, L.; Sansonetti, G.; Micarelli, A. An Empirical Review of Automated Machine Learning. Computers 2021, 10, 11.

AMA Style

Vaccaro L, Sansonetti G, Micarelli A. An Empirical Review of Automated Machine Learning. Computers. 2021; 10(1):11.

Chicago/Turabian Style

Vaccaro, Lorenzo, Giuseppe Sansonetti, and Alessandro Micarelli. 2021. "An Empirical Review of Automated Machine Learning" Computers 10, no. 1: 11.

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