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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F14%3APU111531" target="_blank" >RIV/00216305:26510/14:PU111531 - 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

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

Popis výsledku v původním jazyce

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 generationalgorithm 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 base

Název v anglickém jazyce

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

Popis výsledku anglicky

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 generationalgorithm 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 base

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Crafting Global Competitive Economies: 2020 Vision Strategic Planning & Smart Implementation

ISBN

978-0-9860419-3-8

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

321-327

Název nakladatele

International Business Information Management Association (IBIMA)

Místo vydání

Milan, Italy

Místo konání akce

Milan

Datum konání akce

6. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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