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QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61384399%3A31140%2F23%3A00059034" target="_blank" >RIV/61384399:31140/23:00059034 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007/s10489-022-04370-x" target="_blank" >https://link.springer.com/article/10.1007/s10489-022-04370-x</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10489-022-04370-x" target="_blank" >10.1007/s10489-022-04370-x</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation

  • Original language description

    Main topics of the document: association rule classification; CBA; quantitative association rule learning; rule list optimisation; interpretable machine learning

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Applied intelligence

  • ISSN

    0924-669X

  • e-ISSN

    1573-7497

  • Volume of the periodical

    53

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    DE - GERMANY

  • Number of pages

    31

  • Pages from-to

    20797-20827

  • UT code for WoS article

    000972745500001

  • EID of the result in the Scopus database

    2-s2.0-85153281614