Understanding the influence of features in machine learning is crucial to
interpreting models and selecting the best features for classification. In this
work we propose the use of principles from coalitional game theory to reason
about importance of features. In particular, we propose the use of the Banzhaf
power index as a measure of influence of features on the outcome of a
classifier. We show that features having Banzhaf power index of zero can be
losslessly pruned without damage to classifier accuracy. Computing the power
indices does not require having access to data samples. However, if samples are
available, the indices can be empirically estimated. We compute Banzhaf power
indices for a neural network classifier on real-life data, and compare the
results with gradient-based feature saliency, and coefficients of a logistic
regression model with $L_1$ regularization.

本研究提出利用集合博弈理论的原则来解释和选择分类器最佳特征，并使用 Banzhaf power index 测量特征对分类器结果的影响，结果表明 Banzhaf power index 为零的特征可被无损剪枝以提高分类器性能，无需访问数据样本。