A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
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DOI：
10.1016/j.eswa.2005.12.008
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发表时间：
2007-02-01
影响因子：
8.5
通讯作者：
Fang, Wen-Chang
中科院分区：
文献类型：
--
作者：
Wu, Chih-Hung;Tzeng, Gwo-Hshiung;Fang, Wen-Chang
在建立有效的支持向量机（SVM）模型时，必须仔细地预先确定两个参数C和Q。因此，本研究的目的是开发一种基于遗传的支持向量机（GA-SVM）模型，可以自动确定最佳的参数，C和Q，支持向量机具有最高的预测精度和泛化能力的同时。本文首次采用实值遗传算法优化支持向量机的参数进行破产预测。此外，建议的GA-SVM模型进行了测试，在台湾的金融危机的预测精度进行比较，建议的GA-SVM模型与其他模型的多元统计（DA，logit和probit）和人工智能（NN和SVM）。实验结果表明，GA-SVM模型具有最好的预测精度，这意味着将RGA与传统SVM模型相结合是非常成功的。(C)2005爱思唯尔有限公司保留所有权利。
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.