Difference between revisions of "Publications:Hybrid and ensemble-based soft computing techniques in bankruptcy prediction : a survey"
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| − | |Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], | + | |Name=Verikas, Antanas [av] [0000-0003-2185-8973] (Högskolan i Halmstad [2804], Akademin för informationsteknologi [16904], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Kalsyte, Zivile (Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania) |
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Revision as of 20:47, 30 September 2016
| Title | Hybrid and ensemble-based soft computing techniques in bankruptcy prediction : a survey |
|---|---|
| Author | Antanas Verikas and Marija Bacauskiene and Adas Gelzinis and Zivile Kalsyte |
| Year | 2010 |
| PublicationType | Journal Paper |
| Journal | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1007/s00500-009-0490-5 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:345691 |
| Abstract | 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. |
