A Survey on Recent Advancements in Auto-Machine Learning with a Focus on Feature Engineering

Abstract

A study on the recent trends and progress in the area of Automated Machine Learning (AutoML) is done in detail in this paper. AutoML deals with the end-to-end automation of various steps in a machine-learning pipeline. Some of the steps include feature selection, feature engineering, neural architecture search, hyperparameter optimization, and model selection. The time and the specialized skill set required to perform these tasks may be reduced to some extent with the help of automating all or some of these steps. Thus, a lot of research is going on in the area of AutoML and the recent research articles add justice to the same. A review of existing literature on AutoML with a focus on feature engineering is presented in this paper to assist scientists in building better machine learning models "off the shelf" without extensive data science experience. The use of AutoML in different sectors will also be discussed, as will existing applications of AutoML. A review of published papers, accompanied by describing work in AutoML from a computer science perspective was conducted.