The Data Differentiator Framework (DDF): Quantifying Marginal Data Value with Information-Theoretic and Game-Theoretic Measures in an Agentic Evaluation Pipeline (SDSAE)
Read the original on arXiv Machine Learning →The Data Differentiator Framework (DDF) is a composite scoring method that evaluates candidate data assets by combining a mutual‑information redundancy penalty, a cost‑normalized marginal utility score from an ablation study, and explicit governance inputs such as trust and strategic weights. It is executed through the Strategic Data Sourcing Agent Ecosystem (SDSAE), which bypasses costly model retraining for assets that fail governance or redundancy checks. In a credit‑card fraud benchmark, only the genuinely differentiated asset passed the significance gate and received an acquisition recommendation, while a fully redundant asset scored lowest.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.