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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 4/2020

24.10.2020 | Regular Paper

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

verfasst von: Nazeeh Ghatasheh, Hossam Faris, Ruba Abukhurma, Pedro A. Castillo, Nailah Al-Madi, Antonio M. Mora, Ala’ M. Al-Zoubi, Ahmad Hassanat

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2020

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Abstract

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.
Vorheriger Artikel A new approach for the vanishing gradient problem on sigmoid activation

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Fußnoten
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Metadaten
Titel
Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market
verfasst von
Nazeeh Ghatasheh
Hossam Faris
Ruba Abukhurma
Pedro A. Castillo
Nailah Al-Madi
Antonio M. Mora
Ala’ M. Al-Zoubi
Ahmad Hassanat
Publikationsdatum
24.10.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 4/2020
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-020-00219-x

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