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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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