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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2018

12-05-2016 | Original Article

Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management

Authors: Te-Min Chang, Ming-Fu Hsu

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2018

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Abstract

The deterioration in enterprises’ profitability not only threatens the interests of those firms, but also means related parties (investors, bankers, and stakeholders) could encounter tremendous financial losses, which could also impact the circulation of limited economic resources. Thus, an enterprise risk forecasting mechanism is urgently needed to assist decision-makers in adjusting their operating strategies so as to survive under any highly turbulent economic climate. This research introduces a novel hybrid model that incorporates an incremental filter-wrapper feature subset selection with the statistical examination and twin support vector machine (IFWTSVM) for enterprise operating performance forecasting. To promote a hybrid model’s real-life application, the knowledge visualization extracted from IFWTSVM is represented in an easy-to-grasp style. The experimental results reveal that IFWTSVM’s forecasting quality is very promising for financial risk mining, relative to other forecasting techniques examined in this study.
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Metadata
Title
Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management
Authors
Te-Min Chang
Ming-Fu Hsu
Publication date
12-05-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0545-8

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