...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Decision support systems >Integrated framework for profit-based feature selection and SVM classification in credit scoring
【24h】

Integrated framework for profit-based feature selection and SVM classification in credit scoring

机译：信用评分中基于利润的特征选择和SVM分类的集成框架

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

In this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Types I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals. 2017 Elsevier B.V. All rights reserved.
机译：在本文中，我们提出了一种基于线性支持向量机的利润驱动的分类器构建和同时变量选择方法。主要目标是将与业务相关的信息（例如可变购置成本，I和II型错误成本以及正确分类的实例所产生的利润）纳入建模过程。我们的建议在SVM公式中合并了组惩罚函数，以便同时惩罚属于同一组的变量，假设公司经常以给定的成本获取相关变量组，而不是单独获取它们。智利银行在信用评分问题中研究了拟议的框架，并在与业务相关的目标方面取得了卓越的绩效。 2017 Elsevier B.V.保留所有权利。

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