Date: 2020
Type: Article
Performance assessment of ensemble learning systems in financial data classification
Intelligent systems in accounting finance & management, 2020, Vol. 27, No. 1, pp. 3-9
LAHMIRI, Salim, BEKIROS, Stelios D., GIAKOUMELOU, Anastasia, BEZZINA, Frank, Performance assessment of ensemble learning systems in financial data classification, Intelligent systems in accounting finance & management, 2020, Vol. 27, No. 1, pp. 3-9
- https://hdl.handle.net/1814/70185
Retrieved from Cadmus, EUI Research Repository
Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state-of-the-art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets : one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten-fold cross-validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.
Additional information:
First published online: 23 January 2020
Cadmus permanent link: https://hdl.handle.net/1814/70185
Full-text via DOI: 10.1002/isaf.1460
ISSN: 1055-615X; 1099-1174
Publisher: Wiley
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