📝 SummarXiv

Abstract

High-dimensional datasets require effective feature selection to improve predictive performance, interpretability, and robustness. We propose and evaluate feature selection methods for tabular datasets based on Kolmogorov-Arnold networks (KANs), which parameterize feature transformations through splines, enabling direct access to interpretable importance measures. We introduce four KAN-based selectors ($\textit{KAN-L1}$, $\textit{KAN-L2}$, $\textit{KAN-SI}$, $\textit{KAN-KO}$) and compare them against classical baselines (LASSO, Random Forest, Mutual Information, SVM-RFE) across multiple classification and regression tabular dataset benchmarks. Average (over three retention levels: 20\%, 40\%, and 60\%) F1 scores and $R^2$ score results reveal that KAN-based selectors, particularly $\textit{KAN-L2}$, $\textit{KAN-L1}$, $\textit{KAN-SI}$, and $\textit{KAN-KO}$, are competitive with and sometimes superior to classical baselines in structured and synthetic datasets. However, $\textit{KAN-L1}$ is often too aggressive in regression, removing useful features, while $\textit{KAN-L2}$ underperforms in classification, where simple coefficient shrinkage misses complex feature interactions. $\textit{KAN-L2}$ and $\textit{KAN-SI}$ provide robust performance on noisy regression datasets and heterogeneous datasets, aligning closely with ensemble predictors. In classification tasks, KAN selectors such as $\textit{KAN-L1}$, $\textit{KAN-KO}$, and $\textit{KAN-SI}$ sometimes surpass the other selectors by eliminating redundancy, particularly in high-dimensional multi-class data. 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.