Rough-Fuzzy classifier modeling using data repository sets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F14%3A39898896" target="_blank" >RIV/00216275:25410/14:39898896 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2014.08.152" target="_blank" >http://dx.doi.org/10.1016/j.procs.2014.08.152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2014.08.152" target="_blank" >10.1016/j.procs.2014.08.152</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rough-Fuzzy classifier modeling using data repository sets

Popis výsledku v původním jazyce

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 func

Název v anglickém jazyce

Rough-Fuzzy classifier modeling using data repository sets

Popis výsledku anglicky

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 func

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Computer Science: Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference (KES-2014)

ISBN

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ISSN

1877-0509

e-ISSN

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Počet stran výsledku

9

Strana od-do

701-709

Název nakladatele

Elsevier Science BV

Místo vydání

Amsterdam

Místo konání akce

Gdynia

Datum konání akce

15. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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