Automated Machine Learning -- a brief review at the end of the early years
It provides a foundational overview for researchers and practitioners in machine learning, but it is incremental as a review chapter.
This paper reviews the early years of automated machine learning (AutoML), summarizing its main findings, paradigms, and historical progress in automating stages like feature extraction and model design for supervised learning.
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.