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A Meta-Path-Based Evaluation Method for Enterprise Credit Risk.

Computational intelligence and neuroscience·2022
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A Path-Based Feature Selection Algorithm for Enterprise Credit Risk Evaluation.

Marui Du1, Yue Ma2, Zuoquan Zhang1

  • 1School of Science, Beijing Jiaotong University, Beijing 100044, China.

Computational Intelligence and Neuroscience
|May 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for selecting informative features to assess small and medium-sized enterprises (SMEs) credit risk. The approach effectively ranks both conventional and path-based features for better credit risk evaluation.

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Area of Science:

  • Financial Risk Management
  • Data Science
  • Business Analytics

Background:

  • Measuring credit risk for small and medium-sized enterprises (SMEs) is increasingly complex.
  • Path-based features, reflecting SME interconnections, are valuable for credit risk assessment.
  • Selecting the most informative features from numerous path-based options presents a significant challenge.

Purpose of the Study:

  • To propose a novel feature selection method for SME credit risk evaluation.
  • To effectively rank both conventional and path-based features based on their informativeness.
  • To enhance the efficiency of feature selection through a heuristic algorithm.

Main Methods:

  • A new feature selection method is proposed, considering feature similarity and importance based on structured semantics.
  • The method ranks conventional and path-based features together.
  • A heuristic algorithm is employed for efficient candidate feature searching.

Main Results:

  • The proposed method demonstrates competitive performance compared to state-of-the-art feature selection techniques.
  • Experimental results validate the effectiveness of the novel feature selection approach.
  • The method successfully integrates and ranks diverse feature types.

Conclusions:

  • The developed method offers an effective solution for selecting informative features in SME credit risk analysis.
  • The approach improves the efficiency and accuracy of credit risk evaluation.
  • This work contributes to advancing the methodologies for SME financial risk assessment.