您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Robust Factorization Machines for Credit Default Prediction

文献类型： 会议论文

第一作者： Weijian Ni

作者： Weijian Ni 1 ; Tong Liu 1 ; Qingtian Zeng 1 ; Xianke Zhang 1 ; Hua Duan 1 ; Nengfu Xie 2 ;

作者机构： 1.College of Computer Science and Engineering, Shandong University of Science and Technology

2.Agricultural Information Institute, Chinese Academy of Agricultural Sciences

关键词： Factorization machines;Ramp loss Imbalanced classification;Credit default prediction

会议名称： Pacific Rim International Conference on Artificial Intelligence

主办单位：

页码： 941-953

摘要： Credit default prediction is a topic of great importance in lending industry. Just like many real-world applications, the dataset in the task is often class-imbalanced and noisy, degrading the performance of most machine learning methods. In this paper, we propose an extension of Factorization Machines, named RobustFM, to address the problem of class-imbalance and noisiness in the credit default prediction task. The proposed RobustFM employs a smoothed asymmetric Ramp loss function, into which truncation and hinge parameters are introduced to facilitate noise tolerance and imbalanced learning. Experimental results on several real credit datasets show that RobustFM significantly outperforms state-of-the-art methods in terms of F-measure.

分类号： TP18-53

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台