Aparicio, J., Esteve, M., & Jin, Q. (2024). Machine Learning Techniques and Efficiency Evaluation: A Survey of Methodological Contributions. Analytical Decision Making and Data Envelopment Analysis: Advances and Challenges, 201-234.

This chapter surveys the literature on two types of related contributions. The first group is made up of models devoted to adapting well-known machine learning techniques for estimating production frontiers, satisfying shape constraints (free disposability, convexity, …). The second group consists in approaches that apply frontier estimators to classify observations under the classical framework of supervised machine learning with two or more classes. Thus, this survey represents a round-trip between two relatively unconnected fields to date: machine learning and frontier analysis. In particular, we conduct a review of the existing literature devoted to methodological aims.