2D-Shapley: A Framework for Fragmented Data Valuation
2D-Shapley: A Framework for Fragmented Data Valuation
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DOI：
10.48550/arxiv.2306.10473
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Zhihong Liu;H. Just;Xiangyu Chang;X. Chen;R. Jia
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文献类型：
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作者：
Zhihong Liu;H. Just;Xiangyu Chang;X. Chen;R. Jia
数据估值——量化单个数据源对模型某些预测行为的贡献——对于提高机器学习的透明度以及设计数据共享的激励系统具有重要意义。现有的研究工作主要集中在评估具有共享特征或样本空间的数据源。如何对每个仅包含部分特征和样本的碎片化数据源进行估值仍然是一个未解决的问题。我们首先提出一种计算从聚合数据矩阵中移除一个碎片的反事实情况的方法。基于反事实计算，我们进一步提出2D - 沙普利（2D - Shapley），这是一个碎片化数据估值的理论框架，它在碎片化数据环境中唯一满足一些有吸引力的公理。2D - 沙普利使一系列新的用例成为可能，例如选择有用的数据碎片、为逐个样本的数据值提供解释以及细粒度的数据问题诊断。
Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.