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Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
2010 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 14, no 9, p. 995-1010Article in journal (Refereed) Published
Abstract [en]

This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2010. Vol. 14, no 9, p. 995-1010
Keywords [en]
Bankruptcy prediction, Ensemble, Committee, SVM, Neural network, Fuzzy sets, Decision trees, Case-based reasoning, Genetic algorithms, Rough sets, Hybrid techniques
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-5435DOI: 10.1007/s00500-009-0490-5ISI: 000277013200008Scopus ID: 2-s2.0-77951880059OAI: oai:DiVA.org:hh-5435DiVA, id: diva2:345691
Available from: 2010-08-26 Created: 2010-08-26 Last updated: 2018-01-12Bibliographically approved

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Verikas, Antanas

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