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Undersampling bankruptcy prediction: Taiwan bankruptcy data.

Haoming Wang1, Xiangdong Liu1

  • 1School of Economics, Jinan University, Guangzhou, Guangdong, China.

Plos One
|July 1, 2021
PubMed
Summary

This study addresses data imbalance in bankruptcy prediction using undersampling techniques. Edited Nearest Neighbors (ENN) with Naive Bayes (NB) achieved the best performance, offering a guide for future insolvency modeling.

Area of Science:

  • Financial modeling
  • Data science
  • Machine learning

Background:

  • Machine learning models are crucial for bankruptcy prediction.
  • Data imbalance in historical bankruptcy data leads to inaccurate predictions and economic losses.
  • Undersampling technology's impact on insolvency prediction requires further investigation.

Purpose of the Study:

  • To develop a framework for rapidly evaluating undersampling methods and classification models for bankruptcy prediction.
  • To identify the optimal combination of undersampling technique and classification algorithm for improved insolvency prediction.
  • To analyze the effect of undersampling rates on model performance.

Main Methods:

  • A framework was implemented to systematically test various undersampling methods combined with classification algorithms.

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  • Performance was evaluated using metrics such as the F2-measure.
  • The impact of varying undersampling rates, specifically for cluster centroid-based methods, was analyzed on Linear Discriminant Analysis (LDA) and Naive Bayes (NB) models.

    • Main Results:

    • The combination of Edited Nearest Neighbors (ENN) undersampling with the Naive Bayes (NB) classifier yielded the best performance, achieving an F2-measure of 0.423.
    • Analysis of cluster centroid-based undersampling revealed that performance is sensitive to the undersampling rate.
    • Linear Discriminant Analysis (LDA) showed improved performance at a 30% undersampling rate, indicating non-uniform effects.

    Conclusions:

    • Edited Nearest Neighbors (ENN) followed by Naive Bayes (NB) is a highly effective strategy for mitigating data imbalance in bankruptcy prediction.
    • The undersampling rate significantly influences the performance of certain models like LDA and NB.
    • This research provides valuable insights and a practical guide for optimizing machine learning models in financial distress prediction.