Feature selection in corporate credit rating prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F13%3A39896253" target="_blank" >RIV/00216275:25410/13:39896253 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0950705113002104" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0950705113002104</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.knosys.2013.07.008" target="_blank" >10.1016/j.knosys.2013.07.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Feature selection in corporate credit rating prediction

Popis výsledku v původním jazyce

Credit rating assessment is a complicated process in which many parameters describing a company are taken into consideration and a grade is assigned, which represents the reliability of a potential client. Such assessment is expensive, because domain experts have to be employed to perform the rating. One way of lowering the costs of performing the rating is to use an automated rating procedure. In this paper, we assess several automatic classification methods for credit rating assessment. The methods presented in this paper follow a well-known paradigm of supervised machine learning, where they are first trained on a dataset representing companies with a known credibility, and then applied to companies with unknown credibility. We employed a procedureof feature selection that improved the accuracy of the ratings obtained as a result of classification. In addition, feature selection reduced the number of parameters describing a company that have to be known before the automatic rating

Název v anglickém jazyce

Feature selection in corporate credit rating prediction

Popis výsledku anglicky

Credit rating assessment is a complicated process in which many parameters describing a company are taken into consideration and a grade is assigned, which represents the reliability of a potential client. Such assessment is expensive, because domain experts have to be employed to perform the rating. One way of lowering the costs of performing the rating is to use an automated rating procedure. In this paper, we assess several automatic classification methods for credit rating assessment. The methods presented in this paper follow a well-known paradigm of supervised machine learning, where they are first trained on a dataset representing companies with a known credibility, and then applied to companies with unknown credibility. We employed a procedureof feature selection that improved the accuracy of the ratings obtained as a result of classification. In addition, feature selection reduced the number of parameters describing a company that have to be known before the automatic rating

Druh

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

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

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Svazek periodika

51

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

72-84

Kód UT WoS článku

000324363700007

EID výsledku v databázi Scopus

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