Article2020

Two‐stage hybrid learning techniques for bankruptcy prediction*

16 citations0/0 files storedStatistical Analysis and Data Mining The ASA Data Science Journal

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

Abstract Many machine learning‐based techniques have been used for the prediction of bankruptcy. They can be divided into single, ensemble, and hybrid learning techniques. This paper focuses on a two‐stage hybrid learning approach for bankruptcy prediction where, in the first stage, a clustering algorithm is used to perform the instance selection task in order to filter out a certain number of unrepresentative training data. The clustering results output from the first stage are used with a classification algorithm to construct the prediction model. The results of experiments based on five different country datasets show that the best support vector machine (SVM) classifier performance is obtained using instance selection by affinity propagation (AP) and k‐means individually. Moreover, we also find that although the best AP/k‐means and SVM combination is dataset dependent, the criteria for selecting representative training data are specific. This should become a guideline for developing bankruptcy prediction systems based on the hybrid learning approach.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 2017Machine learning models and bankruptcy prediction 2014Prediction of financial distress: An empirical study of listed Chinese companies using data mining 2013Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches 2011Machine Learning in Financial Crisis Prediction: A Survey 2011Comparative analysis of data mining methods for bankruptcy prediction 2008Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks

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2025Corporate financial distress prediction: a machine learning approach in the era of big data 2022Bankruptcy Prediction using Diverse Machine Learning Algorithms

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