About: Multiple-Criteria Fuzzy Evaluation in FuzzME - Transitions Between Different Aggregation Operators     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)

AttributesValues
rdf:type
rdfs:seeAlso
Description
  • The FuzzME is a software tool for multiple-criteria fuzzy evaluation. The type of evaluation employed in the fuzzy models fully agrees with the paradigm of the fuzzy set theory; the evaluations express the (fuzzy) degrees of fulfillment of the corresponding goals. The basic structure used for the evaluation is called a goals tree. Within the goals tree, the aggregation of partial fuzzy evaluations is done either by one of fuzzified aggregation operators or by a fuzzy expert system. In the FuzzME, the following aggregation methods are supported: fuzzy weighted average, fuzzy OWA operator, fuzzified WOWA operator, fuzzified discrete Choquet integral, and fuzzy expert system. The comprehensive description of the methods and their use in FuzzME has been published. The paper contains a brief description of the methods. The main part of the paper studies the situation when it turns out that the relationship among the criteria is more complex than it was expected. In this case, it would be favorable to replace the original aggregation operator with a more general one with a minimum effort. For this purpose, two algorithms will be presented. The first one allows to derive FNV-measure (fuzzy-number-valued fuzzy measure) for the fuzzy Choquet integral from the parameters of the original aggregation operator (any of the above mentioned aggregation operators can be used). The second algorithm can be used to derive a fuzzy rule base so that the result would be as similar as possible to the original aggregation method.
  • The FuzzME is a software tool for multiple-criteria fuzzy evaluation. The type of evaluation employed in the fuzzy models fully agrees with the paradigm of the fuzzy set theory; the evaluations express the (fuzzy) degrees of fulfillment of the corresponding goals. The basic structure used for the evaluation is called a goals tree. Within the goals tree, the aggregation of partial fuzzy evaluations is done either by one of fuzzified aggregation operators or by a fuzzy expert system. In the FuzzME, the following aggregation methods are supported: fuzzy weighted average, fuzzy OWA operator, fuzzified WOWA operator, fuzzified discrete Choquet integral, and fuzzy expert system. The comprehensive description of the methods and their use in FuzzME has been published. The paper contains a brief description of the methods. The main part of the paper studies the situation when it turns out that the relationship among the criteria is more complex than it was expected. In this case, it would be favorable to replace the original aggregation operator with a more general one with a minimum effort. For this purpose, two algorithms will be presented. The first one allows to derive FNV-measure (fuzzy-number-valued fuzzy measure) for the fuzzy Choquet integral from the parameters of the original aggregation operator (any of the above mentioned aggregation operators can be used). The second algorithm can be used to derive a fuzzy rule base so that the result would be as similar as possible to the original aggregation method. (en)
Title
  • Multiple-Criteria Fuzzy Evaluation in FuzzME - Transitions Between Different Aggregation Operators
  • Multiple-Criteria Fuzzy Evaluation in FuzzME - Transitions Between Different Aggregation Operators (en)
skos:prefLabel
  • Multiple-Criteria Fuzzy Evaluation in FuzzME - Transitions Between Different Aggregation Operators
  • Multiple-Criteria Fuzzy Evaluation in FuzzME - Transitions Between Different Aggregation Operators (en)
skos:notation
  • RIV/61989592:15310/14:33149539!RIV15-GA0-15310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA14-02424S)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 31099
http://linked.open...ai/riv/idVysledku
  • RIV/61989592:15310/14:33149539
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Multiple-criteria evaluation, aggregation operators, software (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [ED8E0AE06875]
http://linked.open...v/mistoKonaniAkce
  • Olomouc
http://linked.open...i/riv/mistoVydani
  • Olomouc
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 32nd International Conference on Mathematical Methods in Economics MME 2014
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holeček, Pavel
  • Talašová, Jana
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Univerzita Palackého v Olomouci
https://schema.org/isbn
  • 978-80-244-4209-9
http://localhost/t...ganizacniJednotka
  • 15310
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
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, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software