Efficient Banzhaf-Based Data Valuation for k-Nearest Neighbors Classification

Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic approaches, such as the Banzhaf value, offer principled frameworks for fair data valuation; however, they suffer from exponential computational complexity. We address this challenge by developing efficient algorithms specifically tailored for computing Banzhaf values in k -nearest neighbor ( k NN) classifiers. We first establish the theoretical hardness of the problem by proving that it is #P-hard. Despite this intractability, we exploit the locality properties of k NN classifiers to develop practical exact algorithms. Our main contribution is a dynamic programming framework that achieves significant computational improvements: we present a pseudo-polynomial algorithm with O ( Wkn 2 ) time complexity for weighted k NN classifiers, where W is the maximum sum of top- k weights, and a specialized algorithm for unweighted k NN that achieves O ( nk 2 ) time complexity, that is, linear in the number of data points. We also offer efficient Monte Carlo estimation methods. Extensive experiments on real-world datasets demonstrate the practical efficiency of our approach and its effectiveness in data valuation applications.