This HTML5 document contains 43 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n15http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
n16http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216275%3A25410%2F14%3A39898896%21RIV15-MSM-25410___/
dctermshttp://purl.org/dc/terms/
n14http://localhost/temp/predkladatel/
n10http://purl.org/net/nknouf/ns/bibtex#
n13http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n6http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n8http://bibframe.org/vocab/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n20http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n17http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216275%3A25410%2F14%3A39898896%21RIV15-MSM-25410___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
This paper reflects the trends of the past years based on the diffusion of various traditional approaches and methods when tackling new problems. Two components of the computational intelligence (CI) are applied, rough and fuzzy sets theory. These components permit one to operate with uncertainty data. The current knowledge in the investigated field is summarized and briefly explained. It also deals with uncertainty in an information system and the two approaches, the fuzzy sets (FSs) and rough sets theory (RST), for operating it. The proposal and implementation of a rough-fuzzy classifier (RFC) is modified. RFC uses the rules generated by RSTbox. The databases IRIS and WINE were chosen for verification. The classification results were compared with the results of other classification methods are applied on these databases. Finally, we summarized the presented problems. Based on the above stated facts it can be claimed that the proposed modified algorithm, RSTbox and RFC model are functional. The model is relatively successful (compared to other approaches), and by using it two classification databases can be carried out. This model is proposed in MATLAB. (C) 2014 Published by Elsevier B.V. This paper reflects the trends of the past years based on the diffusion of various traditional approaches and methods when tackling new problems. Two components of the computational intelligence (CI) are applied, rough and fuzzy sets theory. These components permit one to operate with uncertainty data. The current knowledge in the investigated field is summarized and briefly explained. It also deals with uncertainty in an information system and the two approaches, the fuzzy sets (FSs) and rough sets theory (RST), for operating it. The proposal and implementation of a rough-fuzzy classifier (RFC) is modified. RFC uses the rules generated by RSTbox. The databases IRIS and WINE were chosen for verification. The classification results were compared with the results of other classification methods are applied on these databases. Finally, we summarized the presented problems. Based on the above stated facts it can be claimed that the proposed modified algorithm, RSTbox and RFC model are functional. The model is relatively successful (compared to other approaches), and by using it two classification databases can be carried out. This model is proposed in MATLAB. (C) 2014 Published by Elsevier B.V.
dcterms:title
Rough-Fuzzy classifier modeling using data repository sets Rough-Fuzzy classifier modeling using data repository sets
skos:prefLabel
Rough-Fuzzy classifier modeling using data repository sets Rough-Fuzzy classifier modeling using data repository sets
skos:notation
RIV/00216275:25410/14:39898896!RIV15-MSM-25410___
n3:aktivita
n20:I
n3:aktivity
I
n3:dodaniDat
n17:2015
n3:domaciTvurceVysledku
n13:4032578 n13:7502141
n3:druhVysledku
n12:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
43327
n3:idVysledku
RIV/00216275:25410/14:39898896
n3:jazykVysledku
n11:eng
n3:klicovaSlova
UCI data repository; rules generation; rough-fuzzy approach; fuzzy inference system; Classification
n3:klicoveSlovo
n4:rough-fuzzy%20approach n4:Classification n4:UCI%20data%20repository n4:fuzzy%20inference%20system n4:rules%20generation
n3:kontrolniKodProRIV
[5870259B43C5]
n3:mistoKonaniAkce
Gdynia
n3:mistoVydani
Amsterdam
n3:nazevZdroje
Procedia Computer Science: Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference (KES-2014)
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n17:2014
n3:tvurceVysledku
Jirava, Pavel Křupka, Jiří
n3:typAkce
n15:WRD
n3:zahajeniAkce
2014-09-15+02:00
s:issn
1877-0509
s:numberOfPages
9
n8:doi
10.1016/j.procs.2014.08.152
n10:hasPublisher
Elsevier Science
n14:organizacniJednotka
25410