A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction

Aytuğ Onan

Alphanumeric Journal, 2018, vol. 6, issue 2, 365-376

Abstract: Corporate bankruptcy prediction is an important research direction in finance. Building a robust prediction scheme for bankruptcy can be beneficial to several stakeholders, including management organizations, government and stockholders. Ensemble learning is a well-known technique to improve the predictive performance of classification algorithms by decreasing the generalization error and enhancing the classification accuracy. It has been a well-established technique in bankruptcy prediction to enhance the predictive performance. Diversity plays an essential role in constructing robust ensemble classification schemes. In this paper, a clustering based classifier ensemble approach is presented for corporate bankruptcy prediction. In this scheme, k-means algorithm is utilized to obtain diversified training subsets. Based on the subsets, each base learning algorithms are trained and the predictions of base learning algorithms are combined by a majority voting scheme. In the empirical analysis, four classification algorithms (namely, C4.5 algorithm, k-nearest neighbour algorithm, support vector machines and logistic regression) and three ensemble learning methods (Bagging, AdaBoost and Random Subspace) are evaluated.

Keywords: Clustering; Corporate Bankruptcy Prediction; Diversity; Ensemble Learning (search for similar items in EconPapers)
JEL-codes: C44 C45 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.alphanumericjournal.com/media/Issue/vo ... ye-dayal_0WPdiuh.pdf (application/pdf)
https://alphanumericjournal.com/article/firma-basa ... toplulugu-yaklasimi/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:6:y:2018:i:2:p:365-376

DOI: 10.17093/alphanumeric.333785

Access Statistics for this article

More articles in Alphanumeric Journal from Bahadir Fatih Yildirim
Bibliographic data for series maintained by Bahadir Fatih Yildirim ().