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Statements

Subject Item
n2:RIV%2F00216305%3A26510%2F14%3APU111531%21RIV15-MSM-26510___
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Accurate client (company) information is important for assessing possibility creditworthiness of a client, e.g. in the insurance industry, thus accurate information are not known in real situations. There is always uncertainty in input data which may result in inaccurate decisions. To obtain accurate decision-making rules of client creditworthiness, rough set theory was introduced to obtain knowledge rules for client creditworthiness. Attributes such as type of company, length insurance, insurance penetration, damages (percent) and liquidity (2nd degree) were combined to build a decision table. After unification (discretization and categorization) input value attributes, decision-making rules were calculated through the decision-making rule generation algorithm based on the rough set theory. A classification based on the generated rules classified the client (company) into creditworthy and uncreditworthy groups. The result of fuzzy logic was used to compare with the classification based on the rough s Accurate client (company) information is important for assessing possibility creditworthiness of a client, e.g. in the insurance industry, thus accurate information are not known in real situations. There is always uncertainty in input data which may result in inaccurate decisions. To obtain accurate decision-making rules of client creditworthiness, rough set theory was introduced to obtain knowledge rules for client creditworthiness. Attributes such as type of company, length insurance, insurance penetration, damages (percent) and liquidity (2nd degree) were combined to build a decision table. After unification (discretization and categorization) input value attributes, decision-making rules were calculated through the decision-making rule generation algorithm based on the rough set theory. A classification based on the generated rules classified the client (company) into creditworthy and uncreditworthy groups. The result of fuzzy logic was used to compare with the classification based on the rough s
dcterms:title
Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Creditworthiness case study Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Creditworthiness case study
skos:prefLabel
Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Creditworthiness case study Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Decision-making rules based on rough set theory: Creditworthiness case study
skos:notation
RIV/00216305:26510/14:PU111531!RIV15-MSM-26510___
n3:aktivita
n18:S
n3:aktivity
S
n3:dodaniDat
n11:2015
n3:domaciTvurceVysledku
n5:3663973 n5:1076426
n3:druhVysledku
n15:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
9947
n3:idVysledku
RIV/00216305:26510/14:PU111531
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Rough set, creditworthiness, decision-making rule, decision attribute, condition attribute.
n3:klicoveSlovo
n8:decision-making%20rule n8:condition%20attribute. n8:decision%20attribute n8:creditworthiness n8:Rough%20set
n3:kontrolniKodProRIV
[55FF1B8E307D]
n3:mistoKonaniAkce
Milan
n3:mistoVydani
Milan, Italy
n3:nazevZdroje
Crafting Global Competitive Economies: 2020 Vision Strategic Planning & Smart Implementation
n3:obor
n17:AE
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n11:2014
n3:tvurceVysledku
Doubravský, Karel Doskočil, Radek
n3:typAkce
n6:WRD
n3:zahajeniAkce
2014-11-06+01:00
s:numberOfPages
7
n12:hasPublisher
International Business Information Management Association (IBIMA)
n7:isbn
978-0-9860419-3-8
n13:organizacniJednotka
26510