DEvIANT: discovering significant exceptional (dis-)agreement within groups

Adnene Belfodil, Wouter Duivesteijn, Marc Plantevit, Sylvie Cazalens, Philippe Lamarre

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

    Abstract

    We strive to find contexts (i.e., subgroups of entities) under which exceptional (dis-)agreement occurs among a group of individuals, in any type of data featuring individuals (e.g., parliamentarians, customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns. To handle the sparsity inherent to voting and rating data, we use Krippendorff’s Alpha measure for assessing the agreement among individuals. We devise a branch-and-bound algorithm, named DEvIANT, to discover such patterns. DEvIANT exploits both closure operators and tight optimistic estimates. We derive analytic approximations for the confidence intervals (CIs) associated with patterns for a computationally efficient significance assessment. We prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT.

    Original languageEnglish
    Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
    Subtitle of host publicationEuropean Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I
    EditorsUlf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
    Place of PublicationCham
    PublisherSpringer Nature
    Pages3-20
    Number of pages18
    ISBN (Electronic)978-3-030-46150-8
    ISBN (Print)978-3-030-46149-2
    DOIs
    Publication statusPublished - 2020
    Event19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany
    Duration: 16 Sep 201920 Sep 2019
    http://ecmlpkdd2019.org/

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11906 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
    Abbreviated titleECML PKDD 2019
    CountryGermany
    CityWurzburg
    Period16/09/1920/09/19
    Internet address

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