Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value

• URL: http://arxiv.org/abs/2510.01663v1
• Date: Thu, 02 Oct 2025 04:45:02 GMT
• Title: Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value
• Abstract summary: Kolmogorov-Arnold Networks (KANs) address this by employing learnable spline-based activation functions on edges.<n>We propose ShapKAN, a pruning framework using Shapley value attribution to assess node importance in a shift-invariant manner.<n>Our approach improves KAN's interpretability advantages, facilitating deployment in resource-constrained environments.
• Score: 5.757852060704622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For many real-world applications, understanding feature-outcome relationships is as crucial as achieving high predictive accuracy. While traditional neural networks excel at prediction, their black-box nature obscures underlying functional relationships. Kolmogorov--Arnold Networks (KANs) address this by employing learnable spline-based activation functions on edges, enabling recovery of symbolic representations while maintaining competitive performance. However, KAN's architecture presents unique challenges for network pruning. Conventional magnitude-based methods become unreliable due to sensitivity to input coordinate shifts. We propose \textbf{ShapKAN}, a pruning framework using Shapley value attribution to assess node importance in a shift-invariant manner. Unlike magnitude-based approaches, ShapKAN quantifies each node's actual contribution, ensuring consistent importance rankings regardless of input parameterization. Extensive experiments on synthetic and real-world datasets demonstrate that ShapKAN preserves true node importance while enabling effective network compression. Our approach improves KAN's interpretability advantages, facilitating deployment in resource-constrained environments.

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