Journal Published Online: 14 August 2015
Volume 44, Issue 3

Hybrid Segmentation Strategy and Multi-Agent SVMs for Corporate Risk Management in Class Imbalanced Situations



This study introduced an emerging architecture with a segmentation strategy for the classification of highly imbalanced datasets. The segmentation strategy was specifically performed by K-means, which divided the majority class into some less imbalanced datasets and yielded more robust training data. Superior forecasting performance of the ensemble mechanism/multi-agent mechanism came with a critical drawback, which was that it lacked interpretability. The study further dealt with the obscure nature of the ensemble mechanism by LEM2 algorithm. The human-readable rules could be taken as a guideline for decision makers to make a suitable judgment in a highly competitive financial environment.

Author Information

Chang, Te-Min
Department of Information Management, National Sun Yat-sen Univ., Kaohsiung, Taiwan, CN
Shih, Ching-Hui
Department of Accounting and Information Systems, Asia Univ., Taichung, Taiwan, CN
Hsu, Ming-Fu
Department of Information Management, National Chi Nan Univ., Nantou, Taiwan, CN Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan, CN
Pages: 12
Price: $25.00
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Stock #: JTE20140267
ISSN: 0090-3973
DOI: 10.1520/JTE20140267