About: Learning Business Rules with Association Rule Classifiers

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Learning Business Rules with Association Rule Classifiers Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007%2F978-3-319-09870-8_18
Description	The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide on using discovered rules in the Drools BRMS and on setting the ARC parameters. Experiments performed with modified CBA on several UCI datasets indicate that data coverage rule pruning keeps the number of rules manageable, while not adversely impacting the accuracy. The best results in terms of overall accuracy are obtained using minimum support and confidence thresholds. Disjunction between attribute values seem to provide a desirable balance between accuracy and rule count, while negated literals have not been found beneficial. The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide on using discovered rules in the Drools BRMS and on setting the ARC parameters. Experiments performed with modified CBA on several UCI datasets indicate that data coverage rule pruning keeps the number of rules manageable, while not adversely impacting the accuracy. The best results in terms of overall accuracy are obtained using minimum support and confidence thresholds. Disjunction between attribute values seem to provide a desirable balance between accuracy and rule count, while negated literals have not been found beneficial. (en)
Title	Learning Business Rules with Association Rule Classifiers Learning Business Rules with Association Rule Classifiers (en)
skos:prefLabel	Learning Business Rules with Association Rule Classifiers Learning Business Rules with Association Rule Classifiers (en)
skos:notation	RIV/68407700:21240/14:00219666!RIV15-MSM-21240___
http://linked.open...avai/riv/aktivita	I P S V
http://linked.open...avai/riv/aktivity	I, P(7E12043), S, V
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Kuchař, Jaroslav
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta informačních technologií
http://linked.open...dnocenehoVysledku	25839
http://linked.open...ai/riv/idVysledku	RIV/68407700:21240/14:00219666
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	association rules; rule pruning; business rules; Drools (en)
http://linked.open.../riv/klicoveSlovo	association rules Drools rule pruning business rules
http://linked.open...ontrolniKodProRIV	[77FAA7BA1D60]
http://linked.open...v/mistoKonaniAkce	Prague
http://linked.open...i/riv/mistoVydani	Cham
http://linked.open...i/riv/nazevZdroje	Rules on the Web. From Theory to Applications
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	Linked TV - Television linked to the Web
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Kuchař, Jaroslav Kliegr, T. Vojíř, S. Sottara, D.
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2014-08-18 (xsd:date)
issn	0302-9743
number of pages	15 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-09870-8_18
http://purl.org/ne...btex#hasPublisher	Springer International Publishing AG
https://schema.org/isbn	978-3-319-09869-2
http://localhost/t...ganizacniJednotka	21240

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 8 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software