Work

Interpretable Automated Feature Engineering: A Comprehensive Review with a Focus on Dynamic and Stationary Environments

Year: 2026

Type: article

Abstract: This paper presents a comprehensive review of Automated Feature Engineering (AFE) methods, with a focus on their application in static data environments and explores recent advancements in dynamic Feature Engineering (FE) for evolving data contexts. AFE is essential in modern Machine Learning (ML) pipelines, as it addresses the drawbacks of traditional FE, which often relies heavily on domain expertise and is labor intensive. By automating this process, AFE has the potential to enhance the efficiency, performance, and interpretability of ML workflows. The paper analyzes the methods, strengths, and limitations of different AFE methods. The review also identifies critical research gaps and proposes directions for future advancements. This work serves as a foundational resource for researchers aiming to optimize ML performance through novel AFE strategies.

Source: Artificial Intelligence Review

DOI: 10.1007/s10462-026-11573-y

Authors: Dilshan Sonnadara, Thanh Tung Khuat, Katarzyna Musial-Gabrys, Bogdan Gabrys

Institution: University of Technology Sydney

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: University of Technology Sydney, RMIT University, Australian Research Council