提出首个基于回归的贝扎夫值估计算法，更准更快更稳。
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
- 用回归方法重构贝扎夫值计算，避免传统蒙特卡洛采样
- 在8个数据集上显著提升准确率与特征排序恢复能力
- 理论保证强，适合对模型解释性要求高的场景
贝扎夫值是衡量机器学习模型中特征重要性的流行且可解释的方法，与谢帕利值类似，其精确计算的时间复杂度随特征数呈指数增长，因此需要高效估计方法。现有方法仅限于蒙特卡洛采样，存在效率与稳定性不足的问题。本文提出首个基于回归的贝扎夫值估计器——Kernel Banzhaf，通过新颖的回归公式构建，其精确解即为真实贝扎夫值。受Kernel SHAP启发，该方法高效求解采样后的回归问题。在8个数据集上的实证评估表明，Kernel Banzhaf在准确性、样本效率、抗噪声能力和特征排名恢复方面均显著优于现有蒙特卡洛方法。此外，本文还提供了坚实的理论保证，证明其性能优越。
原文摘要 · Abstract (English)
Banzhaf values provide a popular, interpretable alternative to the widely-used Shapley values for quantifying the importance of features in machine learning models. Like Shapley values, computing Banzhaf values exactly requires time exponential in the number of features, necessitating the use of efficient estimators. Existing estimators, however, are limited to Monte Carlo sampling methods. In this work, we introduce Kernel Banzhaf, the first regression-based estimator for Banzhaf values. Our approach leverages a novel regression formulation, whose exact solution corresponds to the exact Banzhaf values. Inspired by the success of Kernel SHAP for Shapley values, Kernel Banzhaf efficiently solves a sampled instance of this regression problem. Through empirical evaluations across eight datasets, we find that Kernel Banzhaf significantly outperforms existing Monte Carlo methods in terms of accuracy, sample efficiency, robustness to noise, and feature ranking recovery. Finally, we complement our experimental evaluation with strong theoretical guarantees on Kernel Banzhaf's performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。