Fuzzy classification rules based on similarity

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Fuzzy classification rules based on similarity
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GA201%2F08%2F0802" target="_blank" >GA201/08/0802: Applications of Methods of Knowledge Engineering in Data Mining</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Information Technologies - Applications and Theory
ISBN
978-80-971144-0-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
25-31
Publisher name
PONT s.r.o.
Place of publication
Seňa
Event location
Ždiar
Event date
Sep 17, 2012
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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