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P2P Lending Analysis Using the Most Relevant Graph-Based Features

  • Lixin Cui
  • Lu BaiEmail author
  • Yue Wang
  • Xiao Bai
  • Zhihong Zhang
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

Peer-to-Peer (P2P) lending is an online platform to facilitate borrowing and investment transactions. A central problem for these P2P platforms is how to identify the most influential factors that are closely related to the credit risks. This problem is inherently complex due to the various forms of risks and the numerous influencing factors involved. Moreover, raw data of P2P lending are often high-dimension, highly correlated and unstable, making the problem more untractable by traditional statistical and machine learning approaches. To address these problems, we develop a novel filter-based feature selection method for P2P lending analysis. Unlike most traditional feature selection methods that use vectorial features, the proposed method is based on graph-based features and thus incorporates the relationships between pairwise feature samples into the feature selection process. Since the graph-based features are by nature completed weighted graphs, we use the steady state random walk to encapsulate the main characteristics of the graph-based features. Specifically, we compute a probability distribution of the walk visiting the vertices. Furthermore, we measure the discriminant power of each graph-based feature with respect to the target feature, through the Jensen-Shannon divergence measure between the probability distributions from the random walks. We select an optimal subset of features based on the most relevant graph-based features, through the Jensen-Shannon divergence measure. Unlike most existing state-of-the-art feature selection methods, the proposed method can accommodate both continuous and discrete target features. Experiments demonstrate the effectiveness and usefulness of the proposed feature selection algorithm on the problem of P2P lending platforms in China.

Keywords

Feature Selection Credit Risk Credit Rating Feature Selection Method Target Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant nos. 61602535, 61503422 and 61402389), and the Open Projects Program of National Laboratory of Pattern Recognition. Lu Bai is supported by the program for innovation research in Central University of Finance and Economics. Edwin R. Hancock is supported by a Royal Society Wolfson Research Merit Award. Lixin Cui is supported by the Young Scholar Development Fund of Central University of Finance and Economics, No. QJJ1540.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Lixin Cui
    • 1
  • Lu Bai
    • 1
    Email author
  • Yue Wang
    • 1
  • Xiao Bai
    • 2
  • Zhihong Zhang
    • 3
  • Edwin R. Hancock
    • 4
  1. 1.School of InformationCentral University of Finance and EconomicsBeijingChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  3. 3.Software SchoolXiamen UniversityXiamenChina
  4. 4.Department of Computer ScienceUniversity of YorkYorkUK

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