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A novel approach to designing an adaptive committee applied to predicting company’s future performance
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
2013 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 6, p. 2051-2057Article in journal (Refereed) Published
Abstract [en]

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. Current liabilities/Current assets, Total liabilities/Total assets, Net income/Total assets, and Operating Income/Total liabilities are the attributes used in this paper. 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. A random forest is used a basic model in this study. The developed technique was tested on data concerning companies from ten sectors of the healthcare industry of the United States and compared with results obtained from averaging and weighted averaging committees. The proposed adaptivity of a committee size and aggregation weights led to a statistically significant increase in prediction accuracy if compared to other types of committees. © 2012 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2013. Vol. 40, no 6, p. 2051-2057
Keywords [en]
Committee, Random forest, SOM, Data proximity, Financial attribute
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-22974DOI: 10.1016/j.eswa.2012.10.018ISI: 000315607200014Scopus ID: 2-s2.0-84872852144OAI: oai:DiVA.org:hh-22974DiVA, id: diva2:630760
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2018-01-11Bibliographically approved

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