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计算机工程 ›› 2008, Vol. 34 ›› Issue (7): 223-225. doi: 10.3969/j.issn.1000-3428.2008.07.079

• 人工智能及识别技术 • 上一篇    下一篇

基于GA-SVM的企业财务困境预测

岑 涌,钟 萍,罗林开   

  1. (厦门大学信息科学与技术学院,厦门 361005)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-04-05 发布日期:2008-04-05

Prediction Financial Distress of Firms Based on GA-SVM

CEN Yong, ZHONG Ping, LUO Lin-kai   

  1. (School of Information Science and Technology, Xiamen University, Xiamen 361005)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-04-05 Published:2008-04-05

摘要: 通过遗传算法结合支持向量机算法中期望风险边界,对我国上市公司财务数据进行特征提取,并优化构造广义最优分类超平面,从而获得具有较好整体预测性能的联合模型。数值实验表明,该方法可以降低特征空间维数,具有较好的分类准确率。实证结果表明,GA-SVM联合预测模型具有可靠的预测财务困境能力,有着良好的应用前景。

关键词: 遗传算法, 支持向量机, 财务困境, 特征提取

Abstract: This paper uses genetic algorithm and support vector machine to set up a hybrid model of financial distress prediction in Chinese listed firms. Numerical simulation shows that the proposed method can reduce the dimension of the feature space, and has higher correct classification rate. As the result, the proposed GA-SVM hybrid model has reliable financial distress prediction ability, and it has a good application prospect in this area.

Key words: genetic algorithm, support vector machine, financial distress, feature selection

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