Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market

• Published in: Progress in artificial intelligence Vol. 9; no. 4; pp. 361 - 375
• Main Authors: Ghatasheh, Nazeeh, Faris, Hossam, Abukhurma, Ruba, Castillo, Pedro A., Al-Madi, Nailah, Mora, Antonio M., Al-Zoubi, Ala’ M., Hassanat, Ahmad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Bankruptcy; Computational Intelligence; Computer Imaging; Computer Science; Control; Data Mining and Knowledge Discovery; Datasets; Economic models; Mechatronics; Modelling; Natural Language Processing (NLP); Pattern Recognition and Graphics; Prediction models; Regular Paper; Robotics; Vision; Bankruptcy prediction; Cost-sensitive ensemble; Business analytics; Imbalanced data analysis
• ISSN: 2192-6352; 2192-6360
• DOI: 10.1007/s13748-020-00219-x

Bankruptcy is an issue of interest in the business world since decades. It is a crucial endeavor for survival to predict this phenomenon in periods of economic turmoil and recession. In fact, bankruptcy modeling is challenging due to the complexity of contributing factors and the highly imbalanced distribution of available data sets. This work aims at improving the prediction power of bankruptcy modeling, by applying cost-sensitive ensemble methods on a real-world Spanish bankruptcy data set to generate prediction models. The performance of the prediction models is highly competitive in comparison with the related research in the field. Cost-sensitive random forests over-performed other approaches in predicting bankruptcy, achieving a geometric mean of 90.7%, 0.094 and 0.088 type I & type II errors, respectively.