Undersampling bankruptcy prediction: Taiwan bankruptcy data.

Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbal...

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Autores principales:	Haoming Wang, Xiangdong Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e5768835bfc844bbb9e898bdc224e09b
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spelling	oai:doaj.org-article:e5768835bfc844bbb9e898bdc224e09b2021-12-02T20:09:45ZUndersampling bankruptcy prediction: Taiwan bankruptcy data.1932-620310.1371/journal.pone.0254030https://doaj.org/article/e5768835bfc844bbb9e898bdc224e09b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254030https://doaj.org/toc/1932-6203Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbalance problem, but little attention has been paid to the effect of the undersampling technology. Therefore, a framework is used to spot-check algorithms quickly and combine which undersampling method and classification model performs best. The results show that Naive Bayes (NB) after Edited Nearest Neighbors (ENN) has the best performance, with an F2-measure of 0.423. In addition, by changing the undersampling rate of the cluster centroid-based method, we find that the performance of the Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are affected by the undersampling rate. Neither of them is uniformly declining, and LDA has higher performance when the undersampling rate is 30%. This study accordingly provides another perspective and a guide for future design.Haoming WangXiangdong LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254030 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haoming Wang Xiangdong Liu Undersampling bankruptcy prediction: Taiwan bankruptcy data.
description	Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbalance problem, but little attention has been paid to the effect of the undersampling technology. Therefore, a framework is used to spot-check algorithms quickly and combine which undersampling method and classification model performs best. The results show that Naive Bayes (NB) after Edited Nearest Neighbors (ENN) has the best performance, with an F2-measure of 0.423. In addition, by changing the undersampling rate of the cluster centroid-based method, we find that the performance of the Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are affected by the undersampling rate. Neither of them is uniformly declining, and LDA has higher performance when the undersampling rate is 30%. This study accordingly provides another perspective and a guide for future design.
format	article
author	Haoming Wang Xiangdong Liu
author_facet	Haoming Wang Xiangdong Liu
author_sort	Haoming Wang
title	Undersampling bankruptcy prediction: Taiwan bankruptcy data.
title_short	Undersampling bankruptcy prediction: Taiwan bankruptcy data.
title_full	Undersampling bankruptcy prediction: Taiwan bankruptcy data.
title_fullStr	Undersampling bankruptcy prediction: Taiwan bankruptcy data.
title_full_unstemmed	Undersampling bankruptcy prediction: Taiwan bankruptcy data.
title_sort	undersampling bankruptcy prediction: taiwan bankruptcy data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e5768835bfc844bbb9e898bdc224e09b
work_keys_str_mv	AT haomingwang undersamplingbankruptcypredictiontaiwanbankruptcydata AT xiangdongliu undersamplingbankruptcypredictiontaiwanbankruptcydata
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Undersampling bankruptcy prediction: Taiwan bankruptcy data.

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