CEC: Constraints based explanation for classifications

Daniel Deutch, Nave Frost

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Explaining the results of data-intensive computation via provenance has been extensively studied in the literature. We focus here on explaining the output of Machine Learning Classifiers, which are main components of many contemporary Data Science applications. We have developed a simple generic approach for explaining classification results, by looking for constrained perturbations to parts of the input that would have the most significant effect on the classification. Our solution requires white-box access to the model internals and a specification of constraints that define which perturbations are reasonable" in the application domain; both are typically available to the data scientist. We propose to demonstrate CEC, a system prototype that is based on these foundations to provide generic explanations for Neural Networks and Random Forests. We will demonstrate the system usefulness in the context of two application domains: bank marketing campaigns, and visually clear explanations for image classifications. We will highlight the benefit that such explanations could yield to the data scientist and interactively engage the audience in computing and viewing explanations for different cases and different sets of constraints.

Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages1879-1882
Number of pages4
ISBN (Electronic)9781450360142
DOIs
StatePublished - 17 Oct 2018
Externally publishedYes
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Country/TerritoryItaly
CityTorino
Period22/10/1826/10/18

Keywords

  • Data provenance
  • Database constraints theory
  • Supervised learning by classification

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