Metric logic program explanations for complex separator functions

Srijan Kumar, Edoardo Serra, Francesca Spezzano*, V. S. Subrahmanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationScalable Uncertainty Management - 10th International Conference, SUM 2016, Proceedings
EditorsSteven Schockaert, Pierre Senellart
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319458557
StatePublished - 2016
Externally publishedYes
Event10th International Conference on Scalable Uncertainty Management, SUM 2016 - Nice, France
Duration: Sep 21 2016Sep 23 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9858 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Scalable Uncertainty Management, SUM 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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