用沙普利值让KAN网络剪枝更稳定可靠
Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value
- 基于沙普利值评估节点重要性，避免输入坐标变化导致误判
- 在合成与真实数据集上实现高效压缩，保留真实重要节点
- 适合需要可解释性与轻量化部署的AI场景
在诸多实际应用中，理解特征与结果间的关系与高预测准确率同样关键。传统神经网络虽预测能力强，但黑箱特性掩盖了内在函数关系。Kolmogorov-Arnold Networks（KANs）通过在边上传播可学习样条激活函数，既保持竞争力又可恢复符号表达式。然而，其架构对网络剪枝带来独特挑战：传统的基于幅度的方法因对输入坐标平移敏感而不可靠。本文提出ShapKAN，一种基于沙普利值归因的剪枝框架，实现对节点重要性的平移不变评估。相比幅度方法，ShapKAN能精确量化每个节点的实际贡献，确保重要性排序不受输入参数化影响。在合成与真实世界数据集上的大量实验表明，ShapKAN能有效保留真实节点重要性并实现高效网络压缩。该方法强化了KAN的可解释性优势，助力其在资源受限环境中的部署。
原文摘要 · Abstract (English)
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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