Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916985365856256 |
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| author | Fan, Wangxuan Wang, Ching Li, Siqi Liu, Nan |
| author_facet | Fan, Wangxuan Wang, Ching Li, Siqi Liu, Nan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_01663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value Fan, Wangxuan Wang, Ching Li, Siqi Liu, Nan Machine Learning Artificial Intelligence 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. |
| title | Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01663 |