• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Data Mining Workshops >Domain-Specific Adaptation of a Partial Least Squares Regression Model for Loan Defaults Prediction
【24h】

Domain-Specific Adaptation of a Partial Least Squares Regression Model for Loan Defaults Prediction

机译：借助贷款默认预测的域最小二乘性回归模型的域特定调整

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Loan management agencies monitor several loan related attributes for tracking the condition and quality of their financial portfolios. If the trend of loan related status is understood well, the agency would be able to proactively take actions to avoid prolonged delinquency and loan defaults. If an early warning system is available to predict the risk with a loan well-ahead of time, the agency can potentially take corrective measures to prevent the loan from defaulting. In this paper, we use a partial least squares (PLS) regression to model the status of a loan quantized to a non-linear scale of $0$ to $100$ (where the severity function is built with inputs from domain experts). We use the associated ``Variable Influence on Projection'' or VIP scores to select the useful variables for better prediction. In order to address the imbalance in the categories of the observed records (typically the number of low risk records are much more than the risky records), we propose a multi-PLS model for loan prediction. We further enhance the model outputs based on certain domain-specific indicator variables. The resulting model shows improved predictive capacity against a direct application of the PLS model.
机译：贷款管理机构监测若干贷款相关属性，以跟踪其财务投资组合的状况和质量。如果贷款相关地位的趋势众所周知，该机构将能够主动采取行动以避免长期违法和贷款违约。如果预警系统可以预测贷款超出时间的预警系统，原子能机构可能采取纠正措施，以防止贷款违约。在本文中，我们使用部分最小二乘（PLS）回归来模拟量化的贷款状态为0美元的非线性比例为$ 100 $（其中严重函数使用域专家的输入构建）。我们使用关联的“变量影响对投影”或VIP分数来选择有用的变量以获得更好的预测。为了解决观察到的记录类别的不平衡（通常，低风险记录的数量远远超过风险记录），我们提出了一种多项贷款预测模型。我们进一步基于某些域特定的指示器变量来增强模型输出。所得到的模型显示了对PLS模型的直接应用的改进的预测能力。

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