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Subgroup Discovery with Proper Scoring Rules

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

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II
Publisher or commissioning bodySpringer
Pages492-510
Number of pages19
ISBN (Electronic)9783319462271
ISBN (Print)9783319462264
DOIs
DateAccepted/In press - 20 Jun 2016
DateE-pub ahead of print - 4 Sep 2016
DatePublished (current) - 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9852
ISSN (Print)0302-9743

Abstract

Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given population that have unusual distributional characteristics with regard to some target attribute. Such subgroups can be used as a statistical summary which improves on the default summary of stating the overall distribution in the population. A natural way to evaluate such summaries is to quantify the difference between predicted and empirical distribution of the target. In this paper we propose to use proper scoring rules, a well-known family of evaluation measures for assessing the goodness of probability estimators, to obtain theoretically well-founded evaluation measures for subgroup discovery. From this perspective, one subgroup is better than another if it has lower divergence of target probability estimates from the actual labels on average. We demonstrate empirically on both synthetic and real-world data that this leads to higher quality statistical summaries than the existing methods based on measures such as Weighted Relative Accuracy.

    Structured keywords

  • Jean Golding

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Sprimger Verlag at DOI: 10.1007/978-3-319-46227-1_31. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 349 KB, PDF document

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