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 the training of machine learning models.
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This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Pearson, G., Verma, D., de Mel, G. (2019). Value of Information: Quantification and Application to Coalition Machine Learning. In: Calo, S., Bertino, E., Verma, D. (eds) Policy-Based Autonomic Data Governance. Lecture Notes in Computer Science(), vol 11550. Springer, Cham. https://doi.org/10.1007/978-3-030-17277-0_2
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DOI: https://doi.org/10.1007/978-3-030-17277-0_2
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