An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction

Authors:
Lean Yu;Kin Keung Lai;Shouyang Wang
Affiliations:
Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China and College of Business Administration, Hunan University, Changsha 4100 ...;College of Business Administration, Hunan University, Changsha 410082, China and Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China and College of Business Administration, Hunan University, Changsha 4100 ...
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

In this study, a multistage evolutionary programming (EP) based support vector machine (SVM) ensemble model is proposed for designing a corporate bankruptcy prediction system to discriminate healthful firms from bad ones. In the proposed model, a bagging sampling technique is first used to generate different training sets. Based on the different training sets, some different SVM models with different parameters are then trained to formulate different classifiers. Finally, these different SVM classifiers are aggregated into an ensemble output using an EP approach. For illustration, the proposed SVM ensemble model is applied to a real-world corporate failure prediction problem.