Human-in-the-Loop Feature Discovery for Tabular Data
Year: 2024
Type: conference-paper
Abstract: In recent years, researchers have developed several methods to automate discovering datasets and augmenting features for training Machine Learning (ML) models. Together with feature selection, these efforts have paved the way towards what is termed the feature discovery process. Data scientists and engineers use automated feature discovery over tabular datasets to add new features from different sources and enrich training data. By surveying data practitioners, we have observed that automated feature discovery approaches do not allow data scientists to use their domain knowledge during the feature discovery process. In addition, automated feature discovery methods can leak private features or introduce biased ones.
Institution: Delft University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403582468