A SAT-Based Approach to Learn Explainable Decision Sets

Lecture Notes in Artificial Intelligence, pp. 627-645, 2018.

Cited by: 25|Bibtex|Views19|DOI:https://doi.org/10.1007/978-3-319-94205-6_41
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

The successes of machine learning in recent years have triggered a fast growing range of applications. In important settings, including safety critical applications and when transparency of decisions is paramount, accurate predictions do not suffice; one expects the machine learning model to also explain the predictions made, in forms und...More

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