运筹与管理2011，Vol.20Issue(3)：132-139,8.
基于邻域粗糙集属性约简的对偶约束式LS-SVM财务困境预测模型研究
Study on Financial Distress Prediction Model of Least Squares Support Vector Machine of Dual Constraint Type Based on Attribute Reduction of Neighborhood Rough Set
摘要
Abstract
In order to increase the accuracy of financial distress prediction and improve the prediction effect of model, this paper applies neighborhood rough set and genetic algorithm to least squares support vector machine of dual constraint type and advances a prediction model of least squares support vector machine of dual constraint type which is based on attribute reduction of neighborhood rough set.Besides, it presents the procedures of carrying out the improved model.The experimental results show that the model increases its prediction accuracy and reduce its running time by pretreating indicators with neighborhood rough set and optimizating parameters with genetic algorithm.The model is effective in forecasting financial distress.关键词
邻域粗糙集/对偶约束/最小二乘支持向量机/遗传算法/财务困境预测Key words
neighborhood rough set/ dual constraint/ least squares support vector machine/ genetic algorithm/ financial distress prediction分类
管理科学引用本文复制引用
赵冠华..基于邻域粗糙集属性约简的对偶约束式LS-SVM财务困境预测模型研究[J].运筹与管理,2011,20(3):132-139,8.基金项目
国家自然科学基金资助项目(70840018) （70840018）
山东省科技攻关计划项目(2008GG30009005) （2008GG30009005）
山东省软科学研究计划项目(2008RKA223) （2008RKA223）
