AI RESEARCH
Splines-Based Feature Importance in Kolmogorov-Arnold Networks: A Framework for Supervised Tabular Data Dimensionality Reduction
arXiv CS.LG
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ArXi:2509.23366v3 Announce Type: replace Feature selection is a key step in many tabular prediction problems, where multiple candidate variables may be redundant, noisy, or weakly informative. We investigate feature selection based on Kolmogoro-Arnold Networks (KANs), which parameterize feature transformations with splines and expose per-feature importance scores in a natural way.