Automated Machine Learning -- a brief review at the end of the early years

Abstract

Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are therefore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined.1 Please note that although model selection and other efforts for hyperparameter optimization have been out there for decades, see e.g., [24]; this chapter focuses on full model or pipeline selection and design [11,33].

Construct a garbage recognition model using automatic machine learning based on EasyDL

“…They present cutting-edge techniques like Bayesian optimization, reinforcement learning, evolutionary algorithms, and gradient-based approaches, lay out well-known AutoML frameworks, and they draw attention to ongoing AutoML challenges. Escalante [8] summarizes the key discoveries from the early years of the field and provides a historical overview of the development of AutoML. The chapter introduces AutoML for supervised learning, describes the key paradigms, and outlines potential areas for future research.…”

An Empirical Review of Automated Machine Learning

“…This interest has become even more consolidated in recent years with the emergence of Deep Learning, as evidenced by the publication of excellent surveys and reviews on AutoML. Among these, Hugo J. Escalante [22] provides an introduction to AutoML by referring to the overview proposed in [23]. He also presents a historical review in chronological order of the main contributions put forward in the last decade in the context of AutoML for supervised learning.…”