標題: A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
作者: Wu, Chih-Hung
Tzeng, Gwo-Hshiung
Goo, Yeong-Jia
Fang, Wen-Chang
科技管理研究所
Institute of Management of Technology
關鍵字: support vector machine (SVM);real-valued;genetic algorithm (GM);financial distress;prediction;bootstrap simulation
公開日期: 1-Feb-2007
摘要: Two parameters, C and Q, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and Q, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a real-valued genetic algorithm (GA) to optimize the parameters of SVM for predicting bankruptcy. Additionally, the proposed GA-SVM model was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed GA-SVM model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN and SVM). Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful. (C) 2005 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2005.12.008
http://hdl.handle.net/11536/11173
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2005.12.008
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 32
Issue: 2
起始頁: 397
結束頁: 408
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