Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F11%3A39882095" target="_blank" >RIV/00216275:25410/11:39882095 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s00521-010-0495-0" target="_blank" >http://dx.doi.org/10.1007/s00521-010-0495-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-010-0495-0" target="_blank" >10.1007/s00521-010-0495-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning

Popis výsledku v původním jazyce

This paper presents the modelling possibilities of kernel-based approaches to a complex real-world problem, i.e. corporate and municipal credit rating classification. Based on a model design that includes data pre-processing, the labelling of individualparameter vectors using expert knowledge, the design of various support vector machines with supervised learning as well as kernel-based approaches with semi-supervised learning, this modelling is undertaken in order to classify objects into rating classes. The results show that the rating classes assigned to bond issuers can be classified with high classification accuracy using a limited subset of input variables. This holds true for kernel-based approaches with both supervised and semi-supervised learning.

Název v anglickém jazyce

Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning

Popis výsledku anglicky

This paper presents the modelling possibilities of kernel-based approaches to a complex real-world problem, i.e. corporate and municipal credit rating classification. Based on a model design that includes data pre-processing, the labelling of individualparameter vectors using expert knowledge, the design of various support vector machines with supervised learning as well as kernel-based approaches with semi-supervised learning, this modelling is undertaken in order to classify objects into rating classes. The results show that the rating classes assigned to bond issuers can be classified with high classification accuracy using a limited subset of input variables. This holds true for kernel-based approaches with both supervised and semi-supervised learning.

Druh

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

CEP obor

IN - Informatika

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

2011

Kód důvěrnosti údajů

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

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

13

Strana od-do

761-773

Kód UT WoS článku

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EID výsledku v databázi Scopus

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