摘要
为了提高企业财务困境预测的正确率,减少训练模型的样本数和训练时间,在传统支持向量机预测模型的基础上,将Renyi熵和最小二乘支持向量机算法应用于财务困境预测,提出了一种基于Renyi熵的最小二乘支持向量机预测模型。独立推导出了适合财务困境预测这一离散序列的熵以及支持向量机核函数的表达式,同时,给出了这一改进算法的实现步骤。实验结果表明,该算法无论是训练样本的数量还是训练时间,都显著优于传统的最小二乘支持向量机以及标准支持向量机预测模型。
In order to improve the correct rate of enterprise’s financial distress prediction and reduce the sample number and training time, renyi-entropy and least squares support vector machine （LS-SVM） is applied to the field of financial distress prediction on the basis of the traditional support vector machine prediction model and advances a kind of prediction model which is based on Renyi-entropy and LS-SVM. The paper also independently derives the entropy fit for the financial distress prediction which is in discrete sequence, as well as the expression of support vector machine kernel function. Besides, it also presents the procedures of carrying out the improved algorithm. The experimental results show that the improved algorithm is significantly superior to the traditional LS-SVM as well as the standard support vector machine prediction model, regardless of the training samples number or computing time.
出处
《计算机工程与设计》
CSCD
北大核心
2010年第8期1806-1808,1812,共4页
Computer Engineering and Design
基金
国家自然科学基金项目(70840018)
山东省科技攻关计划基金项目(2008GG30009005)
山东省软科学研究计划基金项目(2008RKA223)
关键词
RENYI熵
最小二乘支持向量机
支持向量机
因子分析
财务困境预测
renyi-entropy
least squares support vector machine
support vector machine
gene analysis
financial distress prediction
