计算机应用研究2017，Vol.34Issue(7)：2049-2054,6.DOI:10.3969/j.issn.1001-3695.2017.07.028
引力搜索优化ELM的企业财务危机预警方法
Extreme learning machine based on gravitational search algorithm optimization for enterprise crisis prediction
摘要
Abstract
To improve the precision rate of enterprise financial crisis,this paper developed a novel gravitational search algorithm based kernel extreme learning machine (KELM) parallel model PHGSA-KELM.The model used to solve the problem of feature selection and parameter optimization separately.This paper applied an improved gravitational search algorithm (HGSA) to conduct feature selection and parameter optimization simultaneously.It used a linear-weighted multi-objective function to improve the accuracy of the algorithm,taking into account the average accuracy rate and the subset of feature selection.Moreover,HGSA-KELM model implemented in parallel on multi-core processor,which used OpenMP to speed up the search and optimization process.Real datasets were used to verify this method and the results of simulation,which compared to some similar algorithms.It indicates that this model is better than several other algorithms,and achieves small subset of features.It selects the most related features of enterprise financial crisis,and improves the accuracy and efficiency.Simulation experiments show that the proposed model is effective and efficient.关键词
引力搜索算法/企业危机预警/并行计算/极限学习机/混合模型Key words
gravitational search algorithm(GSA)/enterprise crisis warning/parallel computing/extreme learning machine(ELM)/hybrid model分类
信息技术与安全科学引用本文复制引用
马超..引力搜索优化ELM的企业财务危机预警方法[J].计算机应用研究,2017,34(7):2049-2054,6.基金项目
国家自然科学基金青年基金资助项目(61303113) （61303113）
广东省自然科学基金资助项目(2016A030310072) （2016A030310072）
