New algorithms efficiently value data for kNN classifiers

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

影响 Introduces more efficient methods for understanding data contributions, potentially improving model training and interpretability.

排序理由 Academic paper detailing a new algorithmic approach to a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Banzhaf value
• data valuation
• k-nearest neighbors

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