AI RESEARCH
SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering
arXiv CS.LG
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ArXi:2604.27025v1 Announce Type: cross Automatic feature engineering is an effective approach for improving predictive performance in tabular learning. However, expand-and-reduce methods, such as OpenFE, become increasingly computationally expensive as the input dimensionality grows. This limitation arises primarily from the combinatorial explosion of candidate features generated through operator-feature combinations. To address this issue, we propose SCOPE-FE, a structured search space control framework that improves efficiency by reducing the candidate space prior to feature generation.