Credit rating analysis using adaptive fuzzy rule-based systems: An industry-specific approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F12%3A39895048" target="_blank" >RIV/00216275:25410/12:39895048 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10100-011-0229-0" target="_blank" >http://dx.doi.org/10.1007/s10100-011-0229-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10100-011-0229-0" target="_blank" >10.1007/s10100-011-0229-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Credit rating analysis using adaptive fuzzy rule-based systems: An industry-specific approach

Popis výsledku v původním jazyce

This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of naturallanguage that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network isdesigned to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i. e. specific determinants are used for each industry). The results show that

Název v anglickém jazyce

Credit rating analysis using adaptive fuzzy rule-based systems: An industry-specific approach

Popis výsledku anglicky

This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of naturallanguage that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network isdesigned to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i. e. specific determinants are used for each industry). The results show that

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GP402%2F09%2FP090" target="_blank" >GP402/09/P090: Modelování místních financí metodami výpočetní inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Central European Journal of Operations Research

ISSN

1435-246X

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

14

Strana od-do

421-434

Kód UT WoS článku

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EID výsledku v databázi Scopus

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