• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of intelligent systems in accounting, finance & management >INTEGRATION OF RANDOM SAMPLE SELECTION, SUPPORT VECTOR MACHINES AND ENSEMBLES FOR FINANCIAL RISK FORECASTING WITH AN EMPIRICAL ANALYSIS ON THE NECESSITY OF FEATURE SELECTION
【24h】

INTEGRATION OF RANDOM SAMPLE SELECTION, SUPPORT VECTOR MACHINES AND ENSEMBLES FOR FINANCIAL RISK FORECASTING WITH AN EMPIRICAL ANALYSIS ON THE NECESSITY OF FEATURE SELECTION

机译：集成随机样本选择，支持向量机和用于财务风险预测的信封，并进行特征选择必要性的实证分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Financial risk forecasting (FRF) is an effective tool to help people forecast whether or not a company will fail in future. Among all techniques of FRF, the support vector machine (SVM) is the most newly developed, and one of the most accurate and effective techniques. This study is devoted to investigating an ensemble model of FRF by integrating bagging with an SVM to generate a data-driven SVM ensemble. Bagging is used to produce diverse training datasets on which multiple SVM classifiers are trained to make FRF for a target company. Simple voting is employed to produce a final decision from the SVM model committee. The empirical study has two objectives. One is to verify whether the data-driven SVM ensemble can produce a more dominating performance than the most frequently used techniques in the area of FRF, i.e. multivariate discriminant analysis, logistics regression and a single SVM. The other is to verify whether feature selection is necessary to help the SVM make more precise FRF, although the SVM can handle high-dimensional data. The results indicate that the data-driven SVM ensemble significantly improves the predictive ability of SVM-based FRF. Meanwhile, feature selection can effectively help the SVM achieve better predictive performance, which means that use of feature selection is necessary in SVM-based FRF.
机译：财务风险预测（FRF）是帮助人们预测公司将来是否会倒闭的有效工具。在FRF的所有技术中，支持向量机（SVM）是最新开发的，也是最准确和有效的技术之一。这项研究致力于通过将装袋与SVM集成以生成数据驱动的SVM集成来研究FRF集成模型。 Bagging用于产生多样化的培训数据集，在该数据集上训练了多个SVM分类器以为目标公司制造FRF。通过简单投票，可以得出SVM模型委员会的最终决定。实证研究有两个目标。一种是验证数据驱动的SVM集合是否可以比FRF领域中最常用的技术产生更主要的性能，即多变量判别分析，物流回归和单个SVM。另一个是验证功能选择是否必要，以帮助SVM制作更精确的FRF，尽管SVM可以处理高维数据。结果表明，数据驱动的SVM集成显着提高了基于SVM的FRF的预测能力。同时，特征选择可以有效地帮助SVM实现更好的预测性能，这意味着在基于SVM的FRF中必须使用特征选择。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号