Difference between revisions of "Publications:A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company's Future Performance"
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| − | |Name=Kalsyte, Zivile (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas | + | |Name=Kalsyte, Zivile (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650)) (Kaunas University of Technology, Kaunas, Lithuania);Bacauskiene, Marija (Kaunas University of Technology, Kaunas, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Kaunas, Lithuania) |
|Title=A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance | |Title=A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance | ||
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|HostPublication=International Conference on Computer Research and Development : ICCRD 2013 | |HostPublication=International Conference on Computer Research and Development : ICCRD 2013 | ||
| − | |Conference=International Conference on Computer Research and Development | + | |Conference=5th International Conference on Computer Research and Development (ICCRD 2013), Ho Chi Minh City, Vietnam, February 23-24, 2013 |
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Latest revision as of 21:42, 30 September 2016
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| Title | A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance |
|---|---|
| Author | |
| Year | 2013 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | International Conference on Computer Research and Development : ICCRD 2013 |
| Conference | 5th International Conference on Computer Research and Development (ICCRD 2013), Ho Chi Minh City, Vietnam, February 23-24, 2013 |
| DOI | http://dx.doi.org/10.1115/1.860182_ch11 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:698529 |
| Abstract | This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company’s future performance. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. The proposed technique led to a statistically significant increase in prediction accuracy if compared to other types of committees. |
