基于流形学习的多核SVM财务预警方法研究

在进行财务困境预测时， 为了客观全面地反映企业的财务状况， 纳入较多的预警指标， 数据集维度将变得很大， 传统方法求解此类问题效果并不理想. 流形学习处理高维数据具有较好的降维效果，多核SVM对于分布不平坦的数据具有很好的分类性能. 基于此， 提出了“流形学习+多核SVM”的混合算法财务预警模型， 该模型适用于具有大量指标集的财务预警. 实验结果表明， 与传统预警方法相对比， 其具有更优的预测性能.

Research of multiple kernel SVM based on manifold learning in financial distress prediction

In order to reflect the financial status of enterprises objectively and comprehensively, more indexes will be included and the dimensionality of data set will be too large that traditional methods can't perform well in the process of financial distress prediction. Manifold learning performs well on high dimensional data set, and multiple kernel SVM has excellent classification performance on non-flat data set. Therefore, a hybrid algorithm of financial distress prediction model which integrates multiple kernel learning with manifold learning is proposed, and it can be used in the situation of prediction research with large number of indexes. Experiment results show that this model has better performance than traditional methods.