Predicting Firms' Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning - An Over-Sampling Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F14%3A39898557" target="_blank" >RIV/00216275:25410/14:39898557 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-662-44654-6_3" target="_blank" >http://dx.doi.org/10.1007/978-3-662-44654-6_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-44654-6_3" target="_blank" >10.1007/978-3-662-44654-6_3</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Firms' Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning - An Over-Sampling Approach
Original language description
This paper examines the classification performance of artificial immune systems on the one hand and machine learning and neural networks on the other hand on the problem of forecasting credit ratings of firms. The problem is realized as a two-class problem, for investment and non-investment rating grades. The dataset is usually imbalanced in credit rating predictions. We address the issue by over-sampling the minority class in the training dataset. The experimental results show that this approach leads to significantly higher classification accuracy. Additionally, the use of the ensembles of classifiers makes the prediction even more accurate.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
AE - Management, administration and clerical work
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-10331S" target="_blank" >GA13-10331S: The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 International Conference, AIAI 2014, Rhodes, Greece, September 19-21, 2014, Proceedings
ISBN
978-3-662-44653-9
ISSN
1868-4238
e-ISSN
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Number of pages
10
Pages from-to
29-38
Publisher name
Springer
Place of publication
Heidelberg
Event location
Rhodos
Event date
Sep 19, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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