Rough sets meet statistics - a new view on rough set reasoning about numerical data

Marko Palangetić, Chris Cornelis, Salvatore Greco, Roman Słowiński

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

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    Abstract

    In this paper, we present a new view on how the concept of rough sets may be interpreted in terms of statistics and used for reasoning about numerical data. We show that under specific assumptions, neighborhood based rough approximations may be seen as statistical estimations of certain and possible events. We propose a way of choosing the optimal neighborhood size inspired by statistical theory. We also discuss possible directions for future research on the integration of rough sets and statistics.
    Original languageEnglish
    Title of host publicationRough Sets: IJCRS 2020
    EditorsR. Bello, D. Miao, R. Falcon, M. Nakata, A. Rosete, D. Ciucci
    PublisherSpringer
    Chapter6
    Pages78-92
    Number of pages15
    ISBN (Electronic)978-3-030-52705-1
    ISBN (Print)978-3-030-52704-4
    DOIs
    Publication statusPublished - 7 Jul 2020
    EventInternational Joint Conference on Rough Sets - Havana, Cuba
    Duration: 29 Jun 20203 Jul 2020

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume12179
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceInternational Joint Conference on Rough Sets
    Country/TerritoryCuba
    CityHavana
    Period29/06/203/07/20

    Keywords

    • rough sets
    • statistical learning
    • neighbourhood based rough sets

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