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A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

Author

Listed:
  • Lean Yu

    (Beijing University of Chemical Technology
    University of Chinese Academy of Sciences)

  • Lihang Yu

    (Beijing University of Chemical Technology)

  • Kaitao Yu

    (Canada International School of Beijing)

Abstract

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100

Suggested Citation

  • Lean Yu & Lihang Yu & Kaitao Yu, 2021. "A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    • Handle: RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00249-x
    DOI: 10.1186/s40854-021-00249-x
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    References listed on IDEAS

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    Cited by:

    1. Nana Chai & Baofeng Shi & Bin Meng & Yizhe Dong, 2023. "Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises," SAGE Open, , vol. 13(2), pages 21582440231, April.

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