Fuzzy classification rules based on similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F12%3A00384879" target="_blank" >RIV/67985807:_____/12:00384879 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy classification rules based on similarity

Popis výsledku v původním jazyce

The paper deals with the aggregation of classification rules by means of fuzzy integrals, in particular with the fuzzy measures employed in that aggregation. It points out that the kinds of fuzzy measures commonly encountered in this context do not takeinto account the diversity of classification rules. As a remedy, a new kind of fuzzy measures is proposed, called similarity-aware measures, and several useful properties of such measures are proven. Finally, results of extensive experiments on a numberof benchmark datasets are reported, in which a particular similarity-aware measure was applied to a combination of Choquet or Sugeno integrals with three different ways of creating ensembles of classification rules. In the experiments, the new measure was compared with the traditional Sugeno-measure, to which it was clearly superior.

Název v anglickém jazyce

Fuzzy classification rules based on similarity

Popis výsledku anglicky

The paper deals with the aggregation of classification rules by means of fuzzy integrals, in particular with the fuzzy measures employed in that aggregation. It points out that the kinds of fuzzy measures commonly encountered in this context do not takeinto account the diversity of classification rules. As a remedy, a new kind of fuzzy measures is proposed, called similarity-aware measures, and several useful properties of such measures are proven. Finally, results of extensive experiments on a numberof benchmark datasets are reported, in which a particular similarity-aware measure was applied to a combination of Choquet or Sugeno integrals with three different ways of creating ensembles of classification rules. In the experiments, the new measure was compared with the traditional Sugeno-measure, to which it was clearly superior.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F08%2F0802" target="_blank" >GA201/08/0802: Aplikace metod znalostního inženýrství při dobývání znalostí z databází</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Technologies - Applications and Theory

ISBN

978-80-971144-0-4

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

25-31

Název nakladatele

PONT s.r.o.

Místo vydání

Seňa

Místo konání akce

Ždiar

Datum konání akce

17. 9. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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