A Quantitative Perspective on Values of Domain Knowledge for Machine Learning

Understand

With the exploding popularity of machine learning, domain knowledge in various forms has been playing a crucial role in improving the learning performance, especially when training data is limited.

• Nonetheless, there is little understanding of to what extent domain knowledge can affect a machine learning task from a quantitative perspective.
• To increase the transparency and rigorously explain the role of domain knowledge in machine learning, we study the problem of quantifying the values of domain knowledge in terms of its contribution to the learning performance in the context of informed machine learning.
• We propose a quantification method based on Shapley value that fairly attributes the overall learning performance improvement to different domain knowledge.