@inproceedings{0d94cd175f074226823f2cb5814921d1,
title = "Metric logic program explanations for complex separator functions",
abstract = "There are many classifiers that treat entities to be classified as points in a high-dimensional vector space and then compute a separator S between entities in class +1 from those in class −1. However, such classifiers are usually very hard to explain in plain English to domain experts. We propose Metric Logic Programs (MLPs) which are a fragment of constraint logic programs as a new paradigm for explaining S. We present multiple measures of quality of an MLP and define the problem of finding an MLP-Explanation of S and show that it - and various related problems - are NP-hard. We present the MLP Extract algorithm to extract MLP explanations for S. We show that while our algorithms provide more succinct, simpler, and higher fidelity explanations than association rules that are less expressive, our algorithms do require additional run-time.",
author = "Srijan Kumar and Edoardo Serra and Francesca Spezzano and Subrahmanian, {V. S.}",
note = "Funding Information: Parts of this work were supported by ONR grant N000141612739 and ARO grant W911NF1610342. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 10th International Conference on Scalable Uncertainty Management, SUM 2016 ; Conference date: 21-09-2016 Through 23-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45856-4_14",
language = "English (US)",
isbn = "9783319458557",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "199--213",
editor = "Steven Schockaert and Pierre Senellart",
booktitle = "Scalable Uncertainty Management - 10th International Conference, SUM 2016, Proceedings",
}