Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach

Abstract : 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.
Type de document :
Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.29-38, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_3〉
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01391290
Contributeur : Hal Ifip <>
Soumis le : jeudi 3 novembre 2016 - 10:50:29
Dernière modification le : vendredi 1 décembre 2017 - 01:16:45
Document(s) archivé(s) le : samedi 4 février 2017 - 13:22:58

Fichier

978-3-662-44654-6_3_Chapter.pd...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Petr Hájek, Vladimír Olej. Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.29-38, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_3〉. 〈hal-01391290〉

Partager

Métriques

Consultations de la notice

48

Téléchargements de fichiers

75