Support Vector Regression and Immune Clone Selection Algorithm for Predicting Financial Distress

摘要：

In the analysis of predicting financial distress based on support vector regression (SVR). irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone selection algorithm (ICSA) to optimi/e the parameters of SVR. Additionally, the proposed KSA-SVR model that can automatically determine the optimal parameters was tested on the prediction of financial distress. Then, we compared the proposed ICSA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.

关键词： financial distress rough set immune clone selection algorithm support vector regression prediction

作者: WenJie Tian ManYi Wang

作者单位: Automation Institute BEIJING Union University Beijing, China Finance Institute Capital University of Economics and Business Beijing, China

会议类型: 国际会议

会议名称: The Second International Conference on Business Intelligence and Financial Engineering(BIFE 2009)(第二届商务智能与金融工程国际会议)

会议地点: 北京

会议语种:英文

页码: 130-133

在线出版日期: 2009-07-24（万方平台首次上网日期，不代表论文的发表时间）