Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification
Researchers have developed new algorithms to efficiently calculate the Banzhaf value, a game-theoretic method for data valuation, specifically for k-nearest neighbors (kNN) classifiers. The study proves the computational hardness of the problem but introduces practical exact algorithms using dynamic programming, achieving pseudo-polynomial time complexity for weighted kNN and linear time complexity for unweighted kNN. Experiments on real-world datasets confirm the efficiency and effectiveness of these novel valuation methods. AI
IMPACT Introduces more efficient methods for understanding data contributions, potentially improving model training and interpretability.