Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach
- DOI
- 10.1007/978-3-662-44654-6_3
- Published
- 2014
- Container
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/978-3-662-44654-6_3,
title = {Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach},
author = {Petr Hájek and Vladimír Olej},
year = {2014},
journal = {Lecture Notes in Computer Science},
doi = {10.1007/978-3-662-44654-6_3},
url = {https://doi.org/10.1007/978-3-662-44654-6_3}
}RIS
TY - JOUR TI - Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach AU - Petr Hájek AU - Vladimír Olej PY - 2014 JO - Lecture Notes in Computer Science DO - 10.1007/978-3-662-44654-6_3 UR - https://doi.org/10.1007/978-3-662-44654-6_3 ER -
APA
Hájek, P., & Olej, V. (2014). Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-662-44654-6_3
Source records
- crossref · retrieved 2026-11-10T11:58:05.124Z