2D-Shapley: A Framework for Fragmented Data Valuation

Understand

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.