Value of Information: Quantification and Application to Coalition Machine Learning

Abstract The creation of good machine learning models relies on the availability of good training data. In coalition settings, this training data may be obtained from many different coalition partners. However, due to the difference in the trust level of the coalition partners, the value of the information provided by the coalition partners could be questionable. In this paper, we examine the concept of Value of Information, provide a quantitative measure for it, and show how this can be used to determine the policies for information fusion in coalition machine learning information.
  • Gavin Pearson (Dstl)
  • Dinesh Verma (IBM US)
  • Geeth de Mel (IBM UK)
Date Sep-2018
Venue 2nd International Workshop on Policy-based Autonomic Data Governance (PADG 2018)