Automated feature engineering for automated machine learning
Year: 2025
Type: article
Abstract: Automated feature engineering (AutoFE) and automated machine learning (AutoML) can both be used in a machine learning project to improve the efficiency of a data scientist. In recent years, different algorithms have been developed for both sub fields independently of each other. In this study, the use of AutoFE in combination with AutoML has been evaluated for the first time to determine if AutoFE can increase the model accuracy, while not increasing the computation time. A data fusion meta-learning approach was extended, generalized, and applied to an AutoFE method and then further combined with a pre-existing AutoML method. In the meta-learning approach, more than 150 online data sets were used to create models that recommended the best operator to apply to a certain feature. Using twelve evaluation data sets, we show that combining AutoFE and AutoML is indeed valuable. The accuracy measure used was increased on average by 0.54% compared to using AutoML alone. For multiple data sets, the use of AutoFE significantly outperformed a strategy in which no feature engineering was done, while in the remaining data sets it never significantly performed worse. Therefore, it can be concluded that it is beneficial to combine this computationally efficient AutoFE method with AutoML.
Source: Knowledge-Based Systems
Authors: Casper de Winter, Flavius Frasincar, Bart de Peuter, Vladyslav Matsiiako, Enzo Ido, Jasmijn Klinkhamer
Institution: Erasmus University Rotterdam
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
OpenAlex ID: W4410384337