Title: Splines-Based Feature Importance in Kolmogorov-Arnold Networks: A Framework for Supervised Tabular Data Dimensionality Reduction
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: feature selection, supervised learning, tabular data, KAN

Abstract: High-dimensional datasets require effective feature selection to improve predic
tive performance, interpretability, and robustness. We propose and evaluate fea
ture selection methods based on Kolmogorov-Arnold Networks (KANs), which
parameterize feature transformations through splines, enabling direct access to in
terpretable importance measures. We introduce four KAN-based selectors (KAN-
L1, KAN-L2, KAN-SI, KAN-KO) and compare them against classical baselines
(LASSO, Random Forest, Mutual Information, SVM-RFE) across multiple clas
sification and regression benchmarks. Average (across three feature retention lev
els (20%, 40%, 60%) F1 scores and R2 score Results reveal that KAN-based
selectors, particularly KAN-L2, KAN-L1, KAN-SI, and KAN-KO, are competi
tive with or superior to classical baselines in structured and synthetic datasets.
However, KAN-L1 is often too aggressive in regression, removing useful features,
while KAN-L2 underperforms in classification, where simple coefficient shrinkage
misses complex feature interactions.In classification tasks, KAN selectors such as
KAN-L1, KAN-KO, and KAN-SI sometimes surpass the other selectors by elimi
nating redundancy, particularly in high-dimensional multi-class data. In regres
sion tasks, KAN-L2 and KAN-SI provide robust performance across noisy and
heterogeneous datasets, aligning closely with ensemble predictors. Overall, our
findings demonstrate that KAN-based feature selection provides a powerful and
interpretable alternative to traditional methods, capable of uncovering nonlinear
and multivariate feature relevance beyond sparsity- or impurity-based measures

TL;DR: This paper leverage the spline coefficients  in Kolmogorov Arnold networks through three different strategies  for  feature selection tabular data.