Propositionalization based relational subgroup discovery with RSD

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A03119295" target="_blank" >RIV/68407700:21230/06:03119295 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Propositionalization based relational subgroup discovery with RSD

Original language description

Relational rule learning algorithms are typically designed to construct classification and prediction rules. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach was successfully applied to standard ILP problems (East-West trains, King-Rook-King chess endgame and mutagenicity prediction) and two real-life problems (analysis of telephone calls and traffic accident analysis).

Czech name

Není k dispozici

Czech description

Není k dispozici

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

<a href="/en/project/1K04108" target="_blank" >1K04108: Research and implementation of methods of efficient database propositionalization</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2006

Confidentiality

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

Name of the periodical

Machine Learning

ISSN

0885-6125

e-ISSN

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Volume of the periodical

62

Issue of the periodical within the volume

1-2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

31

Pages from-to

33-63

UT code for WoS article

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EID of the result in the Scopus database

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